🎯 Mark Freeman II

Gable

• Joined as employee-one to lead GTM at Gable to help us reach our first customers.

• Reported directly to the CEO and led the development and execution the go-to-market
strategy which resulted in a successful launch, 4000+ leads, and 300+ meetings with
senior data engineers and leaders, ranging from scaleups to Fortune 500 companies—
this was integral for our ability to raise a Series A. (https://bit.ly/4eDwbPE)

• Co-authored the O’Reilly book "Data Contracts: Developing Production Grade Pipelines at Scale,"
contributing all the technical chapters, and positioning Gable as thought leaders of an
emerging category within the data market. (https://bit.ly/3XIu4mF)

• Served as a technical expert by presenting at conferences on data engineering topics and providing thought leadership via blogs.

• Built Gable’s marketing and sales stack with an emphasis on data capture, automation,
and analytics that enabled automated qualification, conversion into meetings, and nurture
campaigns for leads—while establishing the underlying data model.

• Served as a sales engineer, mapping technical requirements to Gable’s product roadmap,
building champions among directors plus data leaders, creating data strategy decks for
clients, and closing multiple accounts.

• Served as a data scientist, providing insights and visualizations for Gable’s executive team
for presenting sales metrics to the board and potential investors.

Tech Lead, GTM Engineering

On the Mark Data

I help brands connect to data professionals via content strategy, GTM strategy, and influencer marketing. Learn more at onthemarkdata.com.

• Built a brand with an audience of 40k+ data professionals via social media content, technical blogs, and video-- which has harnessed over 14MM impressions.

• Established the weekly Scaling DataOps newsletter, with 7k+ subscribers, where I interview data leaders and executives on data best practices in scaling data infrastructure.

• Built a revenue stream around sponsored content from top data brands such as Nvidia, SuperDataScience, Scale AI, and much more.

Founder

O'Reilly

Currently writing a book on data contracts.

⭐️ "This book has the potential to become the definitive guide for data contracts! I can't wait to dive into the rest of it and unlock even more valuable insights!"

⭐️ "The initial read is clear and provide a good potted history of the data problem and how it has organically morphed due to so many factors. I can't wait for the rest of the book"

⭐️ "This book has the potential to become the bible of data contracts especially as the world moves towards intelligent applications. Data is essential to applications that use AI, and having a good contract is paramount towards establishing trust between systems and having a seamless experience. Looking forward to the full publication!"

Author - Data Contracts: Developing Production Grade Pipelines at Scale

LinkedIn

⭐️ "Took a few hours out of my Saturday to dive into data engineering with DBT, and had the opportunity to pair program with Mark... Great work on the new course, Mark. I sure learned a lot, and I'm confident others will benefit from it as well. As someone who's relatively new to data engineering, this short course was an eye-opener. It expanded my understanding of the scalability and possibilities within the field."

⭐️ "I completed Mark's course over the weekend and thought it was a GREAT introduction to using DBT. For people looking for a low-stakes, easy-to-follow introduction to DBT as a tool for data engineering, I think you would be pressed to find something more effective."

⭐️ "I would like to thank Mark... for creating such a comprehensive, engaging & pure hands on course on dbt. He explained the concepts clearly & demonstrated them with practical examples and exercises."

⭐️ "I was excited when I saw this course on your page, i know this is going to be the course I needed to start my DBT journey and you did not disappoint, Thank you"

LinkedIn Learning Instructor, Data Engineering

Humu

• Build ETL pipelines to bring third-party data sourced from APIs to combine with our product data, creating rich data assets to drive product feature creation and customer analytics.

• Built buy-in, developed the transition plan, and led the migration of the orchestration of data transformations from manually created scheduled queries to data build tool (dbt)-- which enabled version control of our semantic layer, data quality checks, and data discoverability of data assets within our data warehouse.

• Led the development of an internal data quality engine to increase test coverage of core data pipelines and implement data observability.

Senior Data Engineer

Driving forward the organization’s data strategy by:
• 1) Conducting user interviews to understand the organization’s data pain points.
• 2) Identifying the need for data marts to increase data access.
• 3) Scoping overhaul of our data pipelines and warehouse.
• 4) Sourcing data ingestion vendors.
• 5) Fostering leadership buy-in via building minimum viable products.

Senior Data Scientist (Data Engineering)

• Led the development of the Data Science team’s custom R package and managed the internal distribution and upgrading.

• Implemented CI/CD utilizing Docker and CircleCI to ensure changes to the Data Science codebase passes tests and prevent breaks to our internal packages.

• Assisted in doubling the Data Science team via interviewing, onboarding, and technical mentorship for upskilling colleagues.

Senior Data Scientist

• Developed and put into production an NLP pipeline via Python that takes raw text data, classifies respective texts' themes, and ranks and assigns text to relevant insights within customer reports (https://bit.ly/3yAeBqu).

• Designed and put into production via Python an algorithm to classify user activity—output became org’s main product health metric and was validated with both statistical significance and UX research interviews (https://bit.ly/3yt5QhY).

• Support the VP of Product in strategic product decisions by refining business questions, conducting log analytics via SQL and R, and creating reports shared org-wide.

Data Scientist

IndexoRx

• Made it to the final round of interviews for Y Combinator, where we presented our pitch deck to their investment team and described the business opportunity within the pharmacy space in reducing the burden of DIR fees.

• Developed an MVP of our data product that utilized analytics and machine learning to rank users at risk for lowered medication adherence and forecast DIR fees.

• Conducted deep market research on pharmacy business models, pharmacy supply chains, and PBM reimbursement processes.

Founder, Chief Data Officer

Bay Area Community Health Advisory Council

• Lead organization’s evaluation efforts through developing a standardized survey utilizing CSAT and NPS scores, transitioning administration from paper to Google Forms, and providing analysis and data visualizations of services data—resulting in a 24% increase in survey response rate and 2 reports used by org’s Executive Director for fundraising

Member / Evaluation & Analytics Committee Chair

Verana Health

• Utilize deep understanding of medical claims and EHR data, in conjunction with strong SQL and PySpark skills, to work with data that contains over 63 million patients, 274 million patient encounters, and over 10 billion data points amongst 55 separate EHRs

• Developed and deployed into production an algorithm via Apache Spark that classified insurance types for over 250 million records, utilizing unstructured EHR data, that improved both accuracy from 40% to 84% and reduced null values from 16% to <1%

• Support sales team in 21 potential contracts by determining viability of our data assets through defining research and business questions, developing SQL and PySpark code to build cohorts, providing insights on quality issues within data, and creating data reports to communicate findings to non-technical staff

• Conduct an algorithm assessment on a key variable and identified errors impacting 1.3 billion records, as well as provide recommendations to the algorithms team to rectify the error—assessment was utilized to help certify new data architecture

Data Scientist (Data Quality)

Stanford University School of Medicine

• Developed R script for department’s ETL process, that transforms data into cleaned variables and removed duplicate observations based on defined logic—reducing data cleaning time of analysts by 10 hours per project

• Completed an exploratory analysis through conducting 48 multiple regression models to understand relationship between biometric data and self-reported wellbeing data, thus identifying 3 viable hypotheses to further investigate and ensuring the lab diverts resources to impactful studies 

• Updated methods of a previous propensity score match analysis, lowering the SDM for all variables from 0.17-0.21 to <0.10, resulting in the analysis now meeting required match assumptions and improving the manuscript strength for publishing: matching accounts for observed confounder bias

Data Analyst

Collective Health

• Developed and operationalized a Python script, utilizing pandas, that identifies discrepancies between insurance enrollment data and network partner data— reduced manual work process time from 98 to 16.3 hours a year (time saved grows with number of added clients)

• Developed and operationalized a Python script, utilizing pandas, that takes 3 sources of data and creates custom reports for respective insurance partners’ specification— reduced manual work process time from 52 to 4.3 hours a year

• Utilize SQL to pull necessary data to resolve insurance eligibility issues and other related operations work

Operations Associate

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

It didn't feel real until I had the physical copy of the book in my hands, but I'm officially a published author!

This is just such a full circle moment for me as I was reading these O'Reilly animal books when I was dreaming of making a career in data.

If you told me nearly 7 years ago that I would have my name on one I wouldn't believe you!

This represents about two years of life of nights and weekends on top of helping build a startup (Gable) and my own company (On the Mark Data).

Writing a book and building a company was hard... but I would easily do it all over again.

Hundreds of user interviews and discovery calls informed us what would be meaningful in the book.

While the book provided us clear synthesis of what we were seeing in industry.

This created a symbiotic relationship where we would build, write, get feedback, and repeat.

A huge shoutout to the O'Reilly team and my co-authors Chad and Ben!

An even bigger shoutout to everyone who supported us, as it wouldn't have been possible without you all.

Data Engineer | Tech Lead @ Gable.ai | O’Reilly Author: Data Contracts | LinkedIn [in]structor (30k+ Learners) | Founder @ On the Mark Data

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Here's my tech stack for the dev environments I create for LinkedIn Learning courses (all open source) that you can use for yourself:

🐋 Dev Environment:
- Docker Compose - Containers and configs
- VSCode Dev Containers - IDE in container
- GitHub CodeSpaces - Browser cloud compute

📦 Databases:
- Postgres - Transactional Database
- Minio - Data Lake
- DuckDB - Analytical Database

⚙️ Ingestion + Orchestration + Logs:
- Python Scripts - Simplicity over a tool
- Data Build Tool - SQL queries on DuckDB
- Alembic - Python-based database migrations
- Psycopg - Interact with postgres via Python

📋 CI/CD:
GitHub Actions - Simple for students
 
📊 Data:
Data[.]gov - Public real-world datasets

👩🏽‍💻 Coding Surface:
Jupyter Notebooks - Quick and iterative
VS Code - Update and implement scripts

This setup is extremely powerful as you have a full data platform that sets up in minutes, it's filled with real-world data, you can query it right away, and you can see the logs. Plus, since we are using GitHub codespaces, it's essentially free to run in the browser with just a couple clicks!

Innovation isn't about creating completely brand new technologies and practices. Instead, it can be applying a proven method to a new domain.

Under this framing, data engineering teams can learn a lot from clinical research methodologies.

My stats professor in grad school used to joke around that someone messed up on research design if he had to use advanced statistics.

Why? You can avoid jumping through so many technical hoops if you control for people and process!

Here's how I see it applying to data systems:

A - clinical) How do we ensure fidelity (ie research staff follow protocols correctly) across the steps of data capture for the study?

A - data) How do we ensure the business processes for capturing and processing data stays consistent across staff (eg data entry)?

B - clinical) How do we store patient data, and what is the least amount of information we need to reduce risk for a HIPAA violation?

B - data) What data values are considered PII, how do we retain and delete such records, and what regulations do we need to handle?

C - clinical) What are we trying to compare (eg treatment vs control), and thus what measurements and demographics do we need to capture to properly assess this?

C - data) What telemetry do we need on a product to answer questions about usage of a product or service?

Nine times out of ten, the problem you are solving for already has a solution in a different context!

Stop looking at problems at a surface level and instead understand the "pattern" of the problem, and the find similar patterns in spaces where the problem has both been solved and well documented. It's why interdisciplinary teams are the most innovative!

This is also exactly how we landed on "shift left data" in our data contract book by the way! We saw the same problem pattern in DevOps and how they used "shift left" to address it.

"What do you mean my ROI would only be 1.5x? I need it to be a 4x for it to make sense..."

That brutal honesty was one of the best lessons I've learned in getting adoption and budget for data infrastructure projects.

I spent days making a proposal for a c-suite exec I was trying to sell to. I identified their core problems, was getting multiple meetings, and we were talking about numbers... and that's where I messed up.

My mistake was assuming this was their only problem they were considering for purchasing, and not part of a portfolio of problems they were strategically trying to solve.

I wasn't "competing" against a problem I knew I could clearly solve and generate ROI on. Instead, I was competing with the opportunity cost of not pursuing otherr solutions with various multiples of ROI for different problems.

That meeting completely changed how I think about value to execs. It's funny how obvious it sounds looking back, but I'm hard headed and needed to learn by experience.

Fast forward to today, and I feel like I have had enough reps to dive into the business technicals of what value actually means and map it to my experiences.

This week I'm reading the book Valuation, which was essentially the playbook used by McKinsey consultants. Specifically, I want to take my understanding of what is valuable to a business to next level with economic theory and technical business fundamentals.

I think more technical people should study this as well! Move away from parroting the abstract phrase "we need to focus on driving business value" and instead provide a clear plan to what that actually means and why a leader shoukd choose to invest in your initiative over others.

Allowing others to explore AI >>> Gatekeeping AI

I still remember the first time I saw a neural network work in real-time (shoutout the MarI/O Youtube video by SethBling) and just the magic I felt.

That was the moment I realized I needed to make a career in data and the moment I got over my fear of coding.

So many non-technical, but highly skilled in their domain, professionals are now experiencing exactly what I felt years ago.

Why try and police that?

Yeah, that vibe coded app is absolute crap and one agentic action away from deleting its own databases... but that person shipped something.

That's already more progress than the technical naysayers shouting from the sideline.

RBC Capital Markets

- Lead the requirements gathering and creation of data quality rules for a new regulatory initiative (T+1).
- Manage 2 contract workers in drafting requirements, reviewing data lineage and creating be-spoke data quality rules across multiple quality 
 dimensions.
- Built a Tableau report all data quality controls created with meta-data on schema and other useful data elements to help reduce the time in the drafting of new data controls by 20%
- Built a Python script to Extract Deeply Nested JSON from S3 and Tranform into a CSV format for easy processing and analysis. (Avg size of data set is 600MB across 10 different instances with over 10 different critical data elements)

Senior Data Analyst

• Resolved a data quality issue of missing maturity dates on outstanding loans with a material impact of 122 billion USD.
• Collaborated with finance and regulatory reporting stakeholders to implement business requirements, resulting in a 25% increase in data quality and adherence to compliance guidelines as part of an enhanced enterprise data governance framework.
• Utilized Tableau to create an intuitive schema and data quality report, leading to a 35% enhancement in departmental rule design efficiency at RBC Capital Markets.
• Engineered data pipeline processes using Airflow and Python, reducing data processing times by 30% for faster insights delivery and improved efficiency in strategic decision-making.
• Collaborated with Regulatory Reporting and Finance IT stakeholders to enhance data quality and efficiency using DBT, resulting in a 25% reduction in data-related errors.
• Developed SQL data models on a cloud data warehouse and administered a Dremio data lake, handling over 10TB of data.

Data Achievers

•  Worked with a variety of clients to support business intelligence reporting and data quality assurance.
•  Monitored product performance and customer satisfaction metrics to identify areas of improvement, resulting in a 40% increase in user engagement and a 25% increase in revenue.  
• Developed and maintained product roadmaps and feature backlogs, resulting in a 30% increase in team efficiency and a 20% increase in project success rate. 
• Created and maintained product dashboards and reports for job seekers, resulting in a 25% reduction in decision-making time and a 15% increase in data accuracy.

TALENT™ (Ontario Tech Talent)

• Supported a self-paced, self-directed skills development experience (“Self Paced Experience”) for students looking to upskill in data analytics;
• Asynchronously engaged with learners (e.g. discussion forum moderation) via email, chat, or LMS like Canvas
• Provided 1:1 learner support every week; offered as virtual appointments booked through Calendly
• Effectively and efficiently verified customer skill acquisition by way of reviewing all artifacts and other work products presented for verification by learners against approved standards
• Recorded skills verification decisions in TALENT’s system of records

Skill Coach

• Demonstrated exceptional communication skills and exercised diligence in identifying delinquent loans, credit cards, credit lines, or mortgages, and referred clients to Collections.
• Established and maintained relationships with individual or business customers by offering value through every interaction.
• Identify fraudulent transactions at the first point of contact and work with AML and Fraud teams to investigate the transaction to ensure minimal client impact.
• Actioned solutions relevant to client banking needs, with a 30% action rate weekly, to support team goals.

Client Data Analyst

IKEA

- Enhanced order fulfillment Tableau dashboards, improving operational visibility and increasing efficiency by 15%. 
- Analyzed operational data to optimize processes, leading to a 25% improvement in service levels. 
- Collaborated with teams to implement data-driven strategies, reducing inventory errors by 20%

Operations Analyst Intern

Walmart Canada

•	Provided exceptional customer service to clients on the sales floor, ensuring that all their questions are answered promptly and efficiently.
•	Acted as the first point of contact to customers' challenges with finding merchandise on the sales floor.
•	Contributed to team success by supporting colleagues with their workload to effectively deliver on customer promises.
•	Built and maintained a strong relationship with colleagues to improve the quality of work and increase customer satisfaction.
•	Managed safety risk on the sales floor by proactively identifying and removing potential safety issues.

Fulfillment Associate

Fond Regional Pour La Promotion de la Sante du Centre (FRPSC)

• Implemented an analytical framework that reduced transportation costs by 20% by determining the best delivery route for urgent medicine delivery across geographical regions.
• Built dashboards using Tableau to help track Key Performance Indicators (KPIs) thereby increasing visibility on key business metrics.
• Conceptualized a new service delivery design which when implemented improved service levels by 25%.
• Analyzed 6 business processes to collect measurable data.
• Improved the existing demand forecasting software performance by modeling data sources to be compatible with schema.
• Maintained inventory ERP system by ensuring consistency between inventory on-hand and inventory in the system

Logistics Analyst

Cam-Green

• Planned and developed content strategies.
• Built strong relationships with project team members and stakeholders to ensure the achievement
of set goals.
• Assessed business opportunities and develop strategies to attract venture capital.
• Responsible for drafting a business plan for validating the business model.
• Provided consultation on planning and starting new businesses.
• Prepared business proposals and reports for presentation to stakeholders.
• Conceived and implemented a sales development program for the launch of the project which led
to a 110% achievement of the sales target.
• Developed media campaigns across social media outlets and campus bulletins which galvanized
support from key government officials.
• Worked closely with the project manager to ensure that sales and marketing efforts are consistent
with project goals.
• Complaint with established guidelines set by the University Board sponsoring entrepreneurial
initiatives.

Social Media Manager

I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I love to identify hidden business opportunities that lie in data and bring them to life with compelling visualizations.  I spend time setting clear goals, plans of action, and risk mitigation strategies for data analytics projects before even collecting data. 

I am a data analytics professional with a strong background in supply chain and marketing. I am passionate about data engineering. I love a good journey🚘, be it a journey in an RV truck across country, a learning journey📝 , or a customer journey with multiple touch points, or a data pipeline from source system to end user application. I am always curious to know what I can learn about a customer's journey to ensure it is seamless.😊

At RBC I have developed my customer-focused problem-solving mindset at the awareness stage and consideration stages of the business's marketing funnel. I worked closely with partners across the delivery channels (branch and telephone) to deliver exceptional customer service to clients to meet their banking needs. 

At RBC Capital Market, as a Senior Data Analyst, I own the process of ensuring data sent to report owners are fit for purpose (without data quality issues). Build data quality rules using SQL. Build Tableau dashboards to monitor data quality of critical data elements (CDE's) in production 

My experience working with cross-functional teams and creating a sense of synergy in the achievement of a common goal has proven to be a valuable value add. I helped in the resolution of data quality issues by proposing changes to how data is transformed both upstream and downstream.

I am always on the lookout for technologies that improve my workflow; I will take a week or so and dive deep into a data technology to see if it’s a good fit for a side project I'm working on and put aside if not. 

When I'm not working I am with my family making sure we are living every moment and making it count. You can also find me with my headsets listening to the next big rising artist of our generation and exploring new genres of music - I am a big lover of Afrobeats and Cameroonian cuisine 🇨🇲 

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Current Status: Helping folks get their First Data Analyst roles - DM me to learn more

Peter Ndiforchu

I’ll be honest: the hardest skill I had to learn in data had nothing to do with data.

It wasn’t SQL.
It wasn’t Excel.
It wasn’t even storytelling with dashboards.

It was learning to ask the right questions.

Early on, I’d rush to analyze data — build visuals, crunch numbers, automate reports — only to realize I didn’t actually understand what problem I was solving.

That’s when it clicked:
👉 Great analysts don’t just answer questions.
They help clarify them.

Now, before I open any tool, I ask myself:

What decision will this data inform?

Who needs this insight, and why?

What does success really look like?


Once I learned that skill, everything changed.

🎯 Your turn:
What was the hardest skill you had to learn early in your data journey?

---
📌 Some of the hardest skills in data are the most basic skills. RSVP for our upcoming event on Saturday 08/10 to learn more about how to stand out with these skills in a competitive job market: https://luma.com/wphk134i

#52DaysDataChallenge

I help mid/senior level, non-technical professionals get their first data analyst role | Senior Data Analyst @Autodesk |Top rated Data Career Coach on topmate.io

You don’t need a data job to start thinking like an analyst.

A few years ago, I wasn’t a “Data Analyst.”
I was working in warehouse operations and customer service — managing shipments, handling client issues, and keeping things running smoothly.

Back then, I didn’t even know “data analytics” was a career path.
But I noticed something:

Every decision —
How much to stock, when to reorder, which customers to prioritize — was driven by data.

And that curiosity changed everything.

I started asking:
🧠 “What story is this data telling us?”
📊 “How can I use it to make smarter decisions?”

That’s when I began my transition — not by quitting overnight, but by learning step by step:
✅ Mastered Excel and dashboards
✅ Took online data courses after work
✅ Practiced by analyzing data from my own job
✅ Built small projects and shared them online

Slowly, I went from tracking numbers to telling stories with data.
And that shift opened doors to a brand-new career.

If you’re in a non-data role right now, remember that you’re not starting from zero.

You’re bringing problem-solving, people skills, and business insight that analysts use every day.

You just need to layer on the tools and mindset.

So tell me!

💬 What did you do before data?

#52DaysDataChallenge

Most data analysts didn’t take the straight road. They built their careers from the detours.

Every week in this challenge, I meet people who stepped into data through the side doors: 

→Retail floors
→Call centers
→Warehouses
→Operations teams
→Customer service desks
→Every job that required GRIT before SQL.

Some of the most resourceful and business-savvy analysts I know didn’t start with a technical background. 

They started with:

→People 
→Problems
→Messy processes
→High-pressure decisions. 

The mindset came first.
The data skills came later. 

Live #3 will spotlight the career journeys that rarely get told:

→The self-taught
→The late starters
→The career shifters
→The “I didn’t study this” crowd


If you have ever wondered whether your background is an advantage waiting to be reframed, this session will reveal exactly how that transformation happens.

I want your input:

👉 Who should I invite next
Tag someone with an unexpected path into data
Or share the story you want to see featured

Let’s turn this Live into the most relatable conversation of the challenge.

The real test of my data career is not SQL or dashboards.

It is explaining my job to my family around the world.

I’ll jump on a call excited to share something meaningful about a new insight or a successful analysis.

Before I get two sentences in, someone immediately says,
“Peter, you are good with computers. My WhatsApp is acting strange.”

I have explained my work so many times.
Analytics. Forecasting. Helping teams make decisions.

Yet the official family definition has settled at:
“Peter works with numbers and computers. Ask him.”

My mom once introduced me to someone at church with pure confidence.
“My son works in technology. If your phone is not behaving, he can fix it.”

My brother once asked if I could check his internet history from Canada.
He really believed I had that level of power.

And whenever the holidays come around, one relative always waits for the right moment.
“Peter, since you understand computers, my WiFi has been slow since 2019. Can you take a look?”

At this point, I save my explanations for work.
With family, I just help where I can and smile while doing it.

Family: “So you are I.T.?”
Me: “Not exactly”
Family: “Great. The remote stopped working.”

Anyone else living this experience? 😅

#52DaysDataChallenge

🎙️ Essential Data Skills for Career Switchers

When I first started learning data, I had no idea where to begin.
I spent months jumping between Excel tutorials, SQL videos, and random courses — but nothing connected.
It wasn’t until I understood which skills actually mattered that things finally started to click.

That’s why I’m hosting Live #2 of the #52DaysDataChallenge:
👉 “Essential Data Skills for Career Switchers”

In this session, I’ll share:
✅ The foundational data skills every career switcher should master first
✅ Real examples of how these skills show up in the workplace
✅ The biggest learning traps that waste time and delay progress
✅ A simple roadmap to go from confused beginner to confident analyst

👥 Tag someone who’s learning data right now!
Let’s make this journey easier — together.

Darshan Patel, Bukola Oyinlola and 89 other attendees

🎙️ Essential Data Skills for Career Switchers

I didn’t start in data.
And most people who end up in analytics… didn’t either.

When someone asks, “How do I transition into Data Analytics from my old job?”
I always think back to the moments that quietly shaped my own journey.

Most of us made the switch long before we realized we were “doing data.”
We were the ones fixing broken processes…
…tracking numbers when nobody asked…
…building little spreadsheets to make life easier for our team…
…spotting patterns in chaos that no one else noticed.

Your old job didn’t hold you back.
It trained you.

→Teachers already understand assessments and learning outcomes.
→Bankers understand risk, trends, and financial behavior.
→Retail workers understand customer patterns.
→Healthcare workers understand process flow and documentation.
→Engineers understand systems thinking.
→Marketers understand experimentation and attribution.

Every role comes with analytical gold—you just need to translate it.

Here’s the part most people miss:

You don’t transition by abandoning who you were.
You transition by repackaging what you already did.

If you’re trying to break in:
• Identify the analytical parts of your old role.
• Show how you improved something with numbers.
• Highlight moments where you solved problems with data, even informally.
• Build 2–3 small portfolio projects that mirror the jobs you’re applying for.
• Tell your story as someone who’s already been using data… just without the title.

The door into analytics isn’t as far as it looks.
You’ve likely been standing in front of it for years.

What did you do before data?
Share below — your story might inspire someone else.

#52DaysDataChallenge

💻 If Excel had feelings…

It would probably file an HR complaint by now. 😂

Because let’s be honest

We’ve all done some questionable things to our poor spreadsheets:

1️⃣ Renamed the same file 8 times: “Final.xlsx” → “Final2.xlsx” → “Final_ThisOne.xlsx” → “Final_FINAL.xlsx”
2️⃣ Used “Merge & Center” like it’s a design tool 🎨
3️⃣ Written a formula so long even Excel gave up: #NAME?
4️⃣ Pretended we knew why the pivot table disappeared 😅
5️⃣ Watched Excel freeze… and prayed for our unsaved work 🙏
6️⃣ Copied data from a PDF like we were performing surgery with a spoon 🥲

Yet through it all, Excel stays loyal.
No subscription drama. No attitude. Just pure spreadsheet magic.

If Excel could talk, what do you think it would say after seeing your last file? 👀

#52DaysDataChallenge

It wasn’t love at first sight.
At first, data looked complicated. 

Rows. 
Columns. 
Chaos.

My journey started far from spreadsheets, on retail floors, in warehouse stores, and on countless customer service calls.

I didn’t have dashboards or SQL then, just conversations.
But every interaction had something in common: patterns.

I began noticing how certain products sold faster at specific times.

How customers’ frustrations often pointed to deeper issues.

How one small change in process could ripple across an entire team.

Back then, I didn’t call it data.
I called it paying attention to detail.

Over time, I realized those observations were data.
And once I learned how to analyze them, everything changed.

Data stopped being numbers.
It became stories, truths, insights waiting to be uncovered.

That’s what made me fall in love with data.
Its power to give meaning to everyday moments.
To show patterns in chaos.
To help people see what they couldn’t before.

💬 Now I’m curious.
What made you fall in love with data?
Was it a project, a person, or that one “aha” moment when it all clicked?

Someone needs to hear your story today.

Day 17 #52DaysDataChallenge

The most underrated data skill isn’t SQL, Python, or dashboards.

It’s empathy.

I’ve spent years across retail floors, warehouses, and corporate boardrooms… and one pattern always stands out:

The analysts who rise the fastest aren’t just technically strong.

They care enough to understand the human on the other side of the data.

Empathy helps you:
• Ask sharper questions because you understand the real pain behind the request
• Build dashboards people actually use
• Translate complexity without making stakeholders feel small
• Design solutions that fit the workflow, not fight it
• Notice when a number feels “off” because you understand the story of the process

People think empathy is “soft.”
In reality, it’s a strategic advantage.

Every insight we deliver impacts someone’s job, decision, or customer.

When you remember that, your work becomes sharper, more relevant, and significantly more trusted.

The most underrated data skill: empathy.
Agree?

If you took away these 5 tools, my entire week would fall apart.

Here are the core platforms I rely on as a Senior Data Analyst — and why they matter for real business impact:

1. SQL
My decision engine.
This is where I validate narratives, challenge assumptions, and uncover the signals leaders don’t see yet. When the story is unclear, SQL brings the truth to the surface.

2. dbt Labs
Where chaos becomes structure.
Testing, documentation, lineage, and version control — this is how analytics scales beyond a single analyst. dbt turns insights into reliable systems the whole business can trust.

3. Power BI / Looker 
The boardroom translator.
Dashboards aren’t about charts. They’re about influence.
This is where I refine the message, highlight the “why it matters,” and drive decisions that move revenue, cost, or risk.

4. Cursor
The accelerator I didn’t know I needed.
Reusable SQL templates, automated documentation, workflow shortcuts — it’s helped me reclaim hours and reinvest them into deeper, strategic analysis.

5. Jira/Confluence by Atlassian
The operational glue.
Intake, alignment, prioritization, cross-functional visibility — without these, even the best insights get lost in the shuffle. These two keep the work moving and the team aligned.

These tools cover the full lifecycle:

Question → Data → Transformation → Story → Delivery.

Which one should I demo next?

#52DaysDataChallenge

5 Essential Data Analytics Workflow Tools 

Some days, being a Data Analyst feels like being a translator…between English and ‘Data.’

Stakeholder: “I want a dashboard to show user satisfaction — so we can see how pleased our users are with our product.”

Me: “Absolutely! I can build that. Are there any specific KPIs that interest you?”

Stakeholder: “You mean… metrics?”

Me: “Kind of! Metrics are the raw numbers like number of promoters or rate of positive feedback. KPIs are the key ones that tell us if we’re actually meeting our goals like NPS or CSAT.”

Stakeholder: “Got it. Then I’d like to see our NPS and CSAT.”

Me: “Perfect! Those are great KPIs for user satisfaction. What grain would you like to see on the dashboard visuals?”

Stakeholder: “Grain? Like... rice? We’re not an agricultural SaaS, you know.”

Me (internally): Deep breath. Smile. You’re the data whisperer, remember? 😅

Stakeholder: “Not sure what you mean, but I trust your expertise.”

Me: “Fantastic. I’ll take it from here.”

That conversation reminded me that Data Analysts are part translator, part therapist, part magician.

You’re not just building dashboards.
You’re bridging two worlds: business and data.

The best dashboards aren’t the flashiest,  they’re the ones your stakeholders actually understand.

So the next time someone says:

“We need a dashboard!”

Ask them:

→ What decision will this dashboard help you make?

→ Which KPIs matter most?

→ What level of detail (not rice 😄) do you want?

That’s how you turn confusion into clarity — and earn the words every analyst loves to hear:

 “I trust your expertise.” 🙌

#52DaysDataChallenge

Every data pro you admire once stood where you are — at the bottom of the same ladder.

When I first stepped into data analytics, I thought there were only two levels:
✅ “Beginner”
✅ “Expert”

Turns out… there’s an entire ladder in between.
And understanding it changed how I learned, applied, and grew.

Here’s how I now see the Data Career Ladder 👇

1️⃣ Data Curious — You’re exploring what data even is. Watching YouTube videos, reading posts, trying to understand what analysts do.

2️⃣ Data Learner — You’ve picked your tools (Excel, SQL, maybe Python). You’re following tutorials, taking courses, and building your foundation.

3️⃣ Data Doer — You’re applying skills on small projects. You clean messy spreadsheets, build dashboards, or analyze a dataset from Kaggle.

4️⃣ Data Storyteller — You connect insights to impact. You can explain “what the data means” in a way business people understand.

5️⃣ Data Strategist — You go beyond reports. You influence decisions, set up frameworks, and help define what to measure in the first place.

💡 Every step builds on the last — and you can’t skip steps.

When I recognized where I was on this ladder, I stopped comparing myself to others…
and started focusing on the next rung.

---
Your turn:
👉 Where are you on this ladder right now?
👉 What’s one skill helping you climb to the next level?

#52DaysDataChallenge

The Data Career Ladder

Sharing with my network! I've once interviewed for this role and they are a solid team I must say!

Célia Bru is a great data leader!

📈 We're hiring a Senior Data Analyst at KOHO! 📈 

My team needs someone rare: someone who lives at the intersection of deep business thinking and hands-on technical mastery.

You'll own data models in DBT, and create dashboards, but that's just the baseline. What really matters? You understand what drives our business forward and can draw clear connections between technical work and strategic business outcomes. 

The cherry on top: You would get to work with the most caring and fun team I know! ❤️ 

#hiring #dbt #dataanalytics

View the open to hiring details of Célia

The Power of Storytelling in Data Careers. 

This one skill has helped me grow my data analytics career in so many unexpected ways.

What's that one unexpected skill you didn't know would serve you well in your career?

#52DaysDataChallenge

Dashboards behave perfectly… until they sense a stakeholder within 10 feet.

Have you ever tried to convince a toddler to sit still for ONE photo?

That's my relationship with my dashboards before a stakeholder demo

You line up the filters.
You fix the labels.
You triple-check the KPIs.

And for a brief, glorious moment… everything behaves.
Until—
You hit Publish and the dashboard decides to express its true personality.

Suddenly:
→A number you didn’t touch is different.
→A filter you tested 12 times stops working.
→A chart decides today is the day it wants to be “creative.”

And then a stakeholder pops in like:
“Is the data… supposed to do that?”

If you’ve ever had this love–hate relationship, you already know the feeling.

Describe this with ONE emoji.
(I’m ready for the chaos.)

Who can relate?

#52DaysDataChallenge

Unconventional Paths Into Data Analytics 

Most analysts didn’t start in data.
They came from retail, banking, customer service, logistics, hospitality, and so many other places that taught them how to solve real problems long before they touched SQL.

This Live breaks down how people with unconventional backgrounds successfully pivot into analytics — and how your past experience might already give you an edge.

In this session, you’ll learn:
• How to spot “data moments” in your past roles
• Why non-traditional paths often create stronger analysts
• Real stories of pivots that worked
• Practical steps to position yourself for a data role

Date: November 22nd, 2025
Time: 12:15 PM EST

If you’re transitioning into data or guiding others on the journey, this one is for you.

Join us and bring your questions.

Rinu Francis, Bukola Oyinlola and 30 other attendees

Unconventional Paths Into Data Analytics 

If your insight doesn’t change a decision, it’s not an insight. It’s a recap.

How do you write insights that make execs listen?

Most analysts write insights to explain data.
The best analysts write insights to move decisions.

Here’s the pattern I’ve seen across product, finance, and capital markets:

1. Lead with the headline, not the homework.
Your first sentence should answer: “Why does this matter right now?”

2. Translate metrics into money, risk, or time.
Executives think in trade-offs and outcomes—speak their language.

3. Create tension before you reveal the punchline.
Show the gap between the current state and the opportunity.
Tension fuels attention.

4. Strip out every detail that doesn’t change the recommendation.
If it doesn’t move the decision, it doesn’t belong in the slide.

5. End with a clear call: decide, approve, prioritize, or pivot.
Your insight should point to an action—not another meeting.

This simple narrative shift consistently takes conversations from:
“Nice dashboard.”
to
“Okay—what do we do next?”

What’s your storytelling trick when presenting insights?

#52DaysDataChallenge

How Communicate Actionable Insights to Execs

Some days the hardest part of being an analyst isn’t the data… it’s the tools.

We all have that one platform that tests our patience, derails our flow, or sends us straight to Google with a desperate “why isn’t this working?”

So let’s find out:

Which tool frustrates you the most?

I’m running this poll because every frustration has a story behind it — the refresh that never finishes, the merge conflict that ruins your morning, the notebook that suddenly refuses to run the same cell it ran an hour ago.

Vote — then share the specific tool and one thing that really gets under your skin.

Your comment might help someone realize they’re not alone… or point them to a solution you wish you had sooner.

What’s the one tool that tests your patience the most?

#52DaysDataChallenge

Data without context is noise.
Context without curiosity is wasted.

Lately I’ve noticed something across teams I support:

People aren’t short on dashboards.
They’re short on situational awareness.

You can have perfect SQL, clean models, and beautifully designed visuals—
but if you don’t understand the “why behind the numbers,” your insights never move the room.

Executives don’t act because you showed a metric.
They act because you showed meaning.

The strongest analysts I know do three things exceptionally well:

1. Anchor every number to a business moment
A spike doesn’t matter until you explain what triggered it.

2. Pull in what’s not in the data
Market dynamics, customer sentiment, operational constraints, timing.

3. Translate insights into consequences
“If we don’t fix this, here’s what happens.”

That’s when noise becomes narrative.
And narrative becomes action.

What context are you missing lately?

Read any ERD and join tables like a pro — SQL fundamentals

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