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 Analyst

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.

Data Scientist

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 🇨🇲 

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

From Fear to First Data Role — Confidence for Career Switcher

Most people think breaking into data is all about tools.
But the truth?
The real battle happens in your head long before your first interview.

That’s why I’m bringing in Venkata Sai — a standout data analytics creator and respected Lead Data Analyst — to unpack the mindset shift that helped him go from uncertainty to industry leader.

In this session, we’ll explore:

✨ How to rewrite the story you tell yourself
✨ What confidence actually looks like during a career transition
✨ The hidden fears that keep talented people stuck
✨ How to turn imposter syndrome into strategic advantage
✨ And practical ways to communicate your value before your first role

If you’re a career switcher aiming to break into data — this conversation will change how you see your journey.

📅 Join us live on Dec. 7th at 12PM EST
Come ready to learn, reflect, and walk away with renewed confidence and a clearer direction.

See you there.

Peter Ndiforchu

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

Peter Ndiforchu, Immaculata John Ebere and 2 other connections attending

From Fear to First Data Role — Confidence for Career Switcher

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.

AI won’t replace analysts, but analysts who master AI will outpace everyone.

A few months ago, I had a dataset land on my desk that normally would have taken hours to clean, validate, and explore.

You know the kind, messy columns, inconsistent formats, hidden anomalies waiting to trip you up.

Instead of grinding through it, I opened Cursor.

In minutes, it generated a clean transformation script, flagged outliers I had not even spotted yet, and suggested three potential root-cause patterns to investigate.

What used to be an afternoon, sometimes even a full day, was suddenly compressed into a focused 10 minute sprint.

And here is the shift:

Because the grunt work disappeared, I got to spend my time where it actually matters, turning those findings into a narrative, aligning with stakeholders, and shaping decisions.

That is the real edge.

AI does not replace the analyst.
It elevates the analyst who knows how to wield it.

Agree or nah?

#52DaysDataChallenge

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

The longer I stay in data, the more I notice something.
Non-technical skills often determine your real impact.

Two years ago I worked on my first Looker project.
I was new to the tool and focused on building the perfect dashboard.

But early on I noticed something subtle.
My stakeholder kept double checking numbers and pausing before agreeing to anything.

It became clear their real need was not a dashboard.
It was trust in the data.

So I focused on listening to what they were not saying.
I walked them through the data flow and validated numbers together.

Once trust was rebuilt, everything shifted.
The dashboard became a collaboration instead of a deliverable.

It ended up being one of my best projects.
Not because of Looker, but because I paid attention.

For today’s challenge:

📌 What non-technical skill are you working on right now?
Communication? Presence? Boundaries?

Drop yours below.
Someone might need the reminder today.

#52DaysDataChallenge

How I use ChatGPT to speed up data analysis

A few years ago, my workflow lived inside spreadsheets, SQL editors, and endless Slack threads.

Now?
I offload the heavy prep work to ChatGPT so I can focus on the thinking.

Here’s how it plays out in my day:

I paste raw data or a messy export.

I ask for structure, summaries, anomalies, and suggested next steps.

It does in minutes what used to take an hour: hypotheses, data checks, potential flaws, and edge-case questions.

I take that and turn it into insights that influence decisions.

The magic isn’t in replacing the analyst.
It’s in amplifying the analyst’s time so we spend less energy cleaning and more energy guiding the business.

Want my prompt template?

I’ll share it if this gets enough interest.

---
Check out this video in comments from Luke Barousse on how to use ChatGPT for Data Analysis 👇🏾


#52DaysDataChallenge

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

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 

When your stakeholder says “Just make it pretty.”

That’s the moment every analyst questions their career choices.

Because behind that tiny request is a full detective mission:

Untangling vague goals

Reverse-engineering what “pretty” actually means

Negotiating which KPIs matter today (and which magically don’t)

Translating business feelings into data logic

And somehow delivering something useful… while still making it pretty


If you know, you know.

Anyone else?

#52DaysDataChallenge

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

I didn’t realize I was learning the wrong meaning of “consistency” until Day 17 of this challenge.

If I’m being honest, I thought consistency was about discipline.

Show up daily. 
Push something out. 
Repeat.

But somewhere around the halfway mark, I realized something different.

Consistency wasn’t just about showing up.
It was about who I was becoming every time I showed up.

There were days I had ideas bursting out of me.
And there were days I sat staring at a blank screen, wondering why I ever started this challenge in the first place.

But here’s the interesting part:

→Every post strengthened a different muscle.
→Some sharpened my writing.
→Some stretched my creativity.
→Some forced me to confront the stories I’d been hesitant to tell.

And all of them taught me that consistency is less about productivity and more about identity.

You don’t become consistent by pushing harder.
You become consistent by becoming the kind of person who creates even when nobody is clapping yet.

30 days in, and I’ve learned this:

When you commit publicly, your standards rise.
When you share vulnerably, your confidence grows.
When you create repeatedly, your craft transforms.

And the funny thing?
The days I almost skipped were the days that pushed me forward the most.

If you’ve been following this journey from Day 1…
thank you.
You’ve been part of the transformation.

And if you’re joining somewhere in the middle…
we still have 22 days to go.
A lot can change in 22 days.

Who’s been following since Day 1? 👇
I’d love to hear what post has resonated with you the most.

#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

BREAKING: Excel just held its own WWE-style championship… and yes, someone literally became the World Heavy Formula Champion.

After surviving SUMIFS slams, VLOOKUP dropkicks, and a crowd-shaking INDEX-MATCH finisher, the champ, Diarmuid Early walked away with:

🏆A real championship belt
💵 $5,000 in cold, hard cash
📊 And undisputed dominance over every spreadsheet on planet Earth

Rumor has it he celebrated by pressing Ctrl + S… twice.
Can't be too safe when you're holding that kind of gold.

Who’s training for next year? 
I’m perfecting my Pivot Table finishing.

#52DaysDataChallenge

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

AI made me realize this about human judgment.

Working with AI over the past year has taught me something unexpected.

The algorithm is rarely the bottleneck. Our judgment is.

AI can sift millions of rows.
Spot patterns we might miss.
Surface insights in seconds.

But it cannot decide what matters.

It cannot weigh the politics behind a KPI.
It cannot sense when a stakeholder’s concern is really fear disguised as a request.
It cannot choose the trade-offs that shape a business.

That part is still on us.

The deeper I go into AI-powered analytics, the clearer it becomes:

AI amplifies your thinking. It does not replace it.
If your reasoning is sharp, the outputs get sharper.
If your assumptions are weak, the model simply makes your mistakes faster.

So here is the reflection I am sitting with:

👉 How often do we pause to examine the judgment behind our prompts, analyses, and decisions?
👉 Are we upgrading our tools faster than we are upgrading our thinking?

If AI is the accelerator, human judgment is still the steering wheel.

Your turn:
What has AI taught you about your own decision-making?

#52DaysDataChallenge
Day 29

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?

taplio

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