Sr. Data Analyst @ Crunchbase | Founder @ Big Data Energy⚡️ | LinkedIn [in]structor | Empowering career growth in data | Breaking industry stereotypes

💌 partnerships@bdeanalytics.com
Hi 👋🏼 I’m Jess Ramos, the founder of Big Data Energy ⚡️ and a content creator with a community of over 450,000 followers across LinkedIn, YouTube, Instagram, and Substack. I'm an ex-corporate data analyst and data scientist turned entrepreneur. 

I'm also a Top Voice on LinkedIn and one of the leading voices in B:B marketing in tech partnering with big brands like IBM, Claude, Lyft, and the NFL!

After spending years in high-growth tech startups, I realized my true passion is working for myself as a developer advocate striving towards making tech, data, and AI more accessible to all. Through my business, I offer brand partnerships in the form of social media content, conference attendance, and live events. I also teach SQL and data courses for current and aspiring analysts.  

Highlights:

- Creator of Big SQL EnergyⓇ ⚡️ (the world’s greatest SQL course!)
- Partnered with brands like IBM, AWS, NFL, Atlassian, Lyft, Claude, Notion, and Snowflake
- Advocate for making data careers more accessible & human in an AI-driven world

Brands: partnerships@bdeanalytics.com
🎥 Youtube: @JessRamosData
📸 Instagram: @jessramosdata
💌 Newsletter: https://jessramosdata.substack.com/
🤳🏼 TikTok: @jessramosdata

Jess Ramos, MSBA

Everyone thinks when they get a job in data it'll be the most sexiest job ever full of so many intellectual smart things... 

like coding all day, using AI, and being super techy. 

Basically saving the world one query at a time!

But the reality is that data analytics is still a business job because data analysts are parts of businesses and corporations. 

So much of the day revolves around communication, catering to stakeholders, and driving insights that point to higher-level goals and KPIs.

Things that will make the execs happy and look good in front of the board are ultimately the most important tasks-- not the research and exploring you're excited to do with all of your pent-up curiosity. 

If you don't advocate for those tasks and get buy-in from leadership, they're likely a no-go.

Sometimes, it can be draining, but it makes the quiet days where you can explore data and have fun so much more appreciated. 

Is being a data analyst what you expected or not?

Jess Ramos ⚡️

Tech, Data Science, & AI Content Creator w/ 450K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

A popular (male) data influencer has been stealing content and ideas from me and Dawn Choo  

And I'm not very happy about it. 

They copied my LinkedIn cover photo many months ago stealing my original design of "select * from jess_ramos" pointing to my cover photo. 

I didn't say anything because it's the highest form of flattery and it's not worth my time.

But then a week ago, they copied Dawn's instagram video *word for word* stealing the exact concept, text, and script. 

I commented calling them out and they DMed me saying they "tagged Dawn in the comments giving her credit." 

When I said that still doesn't make it okay to copy people, they changed their whole story and said they "filmed it weeks before and Dawn coincidentally came up with the EXACT same idea” copying them.

And they sent a fool proof screenshot with a timestamp they edited in their phone LOL

How about we stop copying other creators and stealing ideas from women??

And even worse, lying about it?

People talk. Is that really how you want your reputation to be?

Tech, Data Science, & AI Content Creator w/ 450K+ followers | Developer Advocate | Big Data Energy⚡️ | Remote Work & Entrepreneurship

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You'll spend 80% of your time data cleaning.

So don't skip these lessons!

It's not as fun and pretty as AI & ML, but it's the bread and butter you need.

Save these functions for later:

↳ Replace(): replace values
↳ Cast(): change data types
↳ Coalesce(): return first non null value
↳ Ifnull(): replace nulls
↳ Concat(): combine string values

And save these videos to improve your skills:
Data Cleaning w/ SQL & AI: https://lnkd.in/enFBW4_5
Handling Nulls the RIGHT way: https://lnkd.in/ehaQky-Z
Recategorizing data w/ Case Statements: https://lnkd.in/eDPgdcWv

Data cleaning in SQL for data science and data analytics doesn’t have to be hard 🔥

What are your favorite functions for data cleaning??

♻️ repost for your network and save this for later!

Top 5 SQL Data Cleaning Functions

Learning how to set corporate boundaries professionally is truly an art. If you don't learn this... you'll likely be pushed around and taken advantage of. 

These are boundaries that I set for myself (and have accidentally broken in the past!):

1. Do not respond to any notifications or messages outside of your working hours. You never want to set the precedent and expectations that you will— because then if you ever don’t, your manager will feel let down and disappointed in you.

2. Overcommunicate and manage up. If your manager wants to be super hands on, keep them super updated with frequent status updates and decisions. That way they can weigh in if they want to and will build trust in you. And you can keep them from pestering you daily with updates.

3. Take time for yourself and enjoy your personal life (& PTO) GUILT FREE. Just because they have no hobbies doesn’t mean you can’t enjoy your life. You shouldn't feel guilty for using your benefits and not working past your contracted hours.

Have you had a manager like this? Are you healing from one like this?

Tech, Data Science, & AI Content Creator w/ 400K+ followers | Big Data Energy⚡️| Technical Educator | Remote Work & Entrepreneurship

Is data science still the sexiest job of the 21st century?

It’s changed a lot over the years: 

↳ 2012: DS was deemed as the sexiest job of the 21st century— and it was. It required a phd and deep statistical modeling knowledge resulting in very high salaries and demand.  

↳ 2021: This was the rise of bootcamps that over promised and underdelivered and the market became more saturated. However, tech companies were hiring so much, no one really noticed the oversaturation and many people broke in. 

↳ 2026: Hiring has slowed down greatly and so has the demand to become a data scientist. Everything has been in a lull feeling slow, and people are having to adapt their skills to include AI & agents to get jobs. 

The field isn’t dying, it’s just adapting and reshaping into new kinds of roles. 

Skills and tools are changing (as always), and you should only go into a data role if you actually enjoy it!

I think it’s worth it, what about you?

♻️ Repost for your network and 🏷️ save this for later!

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

I help immigrants and career-switchers land analyst roles—without burning out on certificates.

By day I build decision-grade analytics (currently at Autodesk). By night I run Data Career Compass, where we treat your job search like a product launch:
Market → Positioning → Portfolio → Distribution → Interviews.

My POV:
Most candidates try to add more tools. Winners learn to package their value. Your portfolio isn’t a gallery; it’s a sales page for your judgment.

Receipts:

→ Professionals land interviews by aligning projects to business outcomes (not toy datasets).

→ My frameworks have been used across finance, tech, and education to translate experience into results.

→ Built data products and controls in regulated environments (RBC Capital Markets) that leaders rely on daily.


What you’ll get here:

→ Weekly breakdowns of real projects (SQL, Python, dbt, DuckDB, Looker/Tableau/Power BI).

→ Storytelling patterns that turn insights into action.

→ Templates that remove friction: portfolio briefs, stakeholder one-pagers, interview scorecards.

Coming February:

I’m launching a coaching program designed for mid-level and senior professionals ready to transition into analytics.

This isn’t an entry-level bootcamp—it’s a career accelerator that helps you reposition your existing experience, communicate business value, and step confidently into data-driven roles.

Work with me:

↳ Cohorts + 1:1 coaching → DM “HIRED”

↳ January program for mid/senior professionals → DM “JANUARY” for early access

↳ Guest workshops for teams and communities → DM “WORKSHOP”


I build analysts who think like product managers and communicate like consultants.

Peter Ndiforchu

Most candidates think they’re being evaluated on what they built.

They’re not.

When you explain a project, hiring managers are quietly assessing something much deeper.

Here’s what’s actually happening behind the scenes 👇

1️⃣ Can you think in business outcomes—or just tasks?
If your story starts with “I used SQL to…” and ends there, that’s a red flag.
They’re listening for why the project existed and what changed because of it.

2️⃣ Do you understand the problem—or just the solution?
Strong candidates explain the constraint, the tradeoff, and the decision.
Weak ones jump straight to the dashboard.

3️⃣ Can you simplify complexity?
If you can’t explain your project to a non-technical stakeholder in 60 seconds,
they assume you’ll struggle in real meetings.

4️⃣ Did you drive the work—or were you just assigned it?
Ownership shows up in language:

“I noticed…”

“I proposed…”

“I pushed back when…”


That’s leadership without the title.

5️⃣ Do you sound like someone they can put in front of executives?
Clarity. Structure. Calm confidence.
That’s what earns trust.

Here’s the uncomfortable truth:

Two candidates can build the same project.
Only one gets the offer—because they frame it differently.

Your projects don’t get you hired.
Your interpretation of them does.

If this resonated, next time you explain a project, ask yourself:
“Am I telling a story—or reciting a task list?”

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

What Data Hiring Managers Really Evaluate on Interviews

Most candidates know the STAR method.
Very few use it in a way that actually signals seniority in data roles.

Here’s how hiring managers experience it 👇

The STAR method for data roles (with a real example)

S — Situation
❌ Weak: “We didn’t have good dashboards.”
✅ Strong: “Sales leaders were making weekly decisions using spreadsheets pulled manually from three systems.”

Why this matters:
You’re showing business context, not just a messy data problem.

T — Task
❌ Weak: “I was asked to build a dashboard.”
✅ Strong: “I was responsible for creating a single source of truth that leadership could trust for forecasting.”

Why this matters:
This reframes you from executor to owner.

A — Action
❌ Weak: “I used SQL and Tableau.”
✅ Strong:
“I audited existing tables, wrote validation queries to reconcile numbers across sources, aligned definitions with Sales Ops, and designed a dashboard focused on decision-making—not vanity metrics.”

Why this matters:
Tools are expected. Judgment is not.

R — Result
❌ Weak: “They liked the dashboard.”
✅ Strong:
“Forecast variance dropped by 18%, manual reporting time was reduced by 6 hours per week, and the dashboard became the default reference in leadership meetings.”

Why this matters:
Impact beats effort. Every time.

The real mistake candidates make:
They treat STAR like a storytelling template.
Hiring managers hear it as a decision-making framework.

In data roles, STAR isn’t about what you built.
It’s about:

How you understood the business

How you made trade-offs

How your work changed behavior


If your STAR answers don’t reveal those signals,
you’re underselling yourself—no matter how strong your skills are.

📌 Save this before your next interview.

How I went from Cameroon → Canada → a US data role (without ‘local experience’).

When I moved, I assumed the biggest hurdle would be visas, credentials, or “local experience.”

It wasn’t.

The real question global recruiters were silently asking was:
“Who can vouch for this person when we can’t observe them directly?”

Here’s what made the difference on my journey 👇

1/ Relationships reduce hiring risk
Recruiters outside your country can’t see your work day-to-day. They rely on people they trust to validate your judgment and impact.
I focused on building genuine connections with:

Hiring managers

Senior data practitioners

People in the ecosystem who could confidently say,

 “I’ve worked with him. He delivers. I’d hire him again.”

2/ Visibility beats invisibility
I didn’t just rely on a resume. I made my work and thinking visible by:

Sharing real projects publicly

Discussing trade-offs and decisions

Engaging with communities in LinkedIn, Slack, and Discord

This gave recruiters context before even meeting me.

3/ Skills matter, but signals win
SQL, Python, Excel—all necessary. But what got me hired was showing:

Structured thinking

Clear communication of impact

Ability to navigate ambiguity

Coupled with endorsements from people who could vouch for me, geography became irrelevant.

Global hiring isn’t about ‘local experience’—it’s about trusted signals.

Ask yourself: “Who in this market can confidently vouch for my work?”

That’s the real game.

Cameroon → Canada → a US data role

The first time I realized hiring expectations weren’t universal…
I thought I had done everything right.

Strong technical skills.
Solid projects.
Clear results.

Then I walked into an interview outside my home market and something felt off.

They weren’t impressed by what tools I knew.
They kept probing how I worked.

Questions like:

“How did you influence stakeholders who didn’t agree with you?”

“What trade-offs did you make under pressure?”

“Who trusted your judgment and why?”


That’s when it clicked.

In some markets, hiring is about capability.
In others, it’s about credibility.
And in global roles, it’s about signal alignment.

Same resume.
Same experience.
Very different evaluation lens.

I stopped optimizing for: 
❌ tool checklists
❌ keyword stuffing
❌ “local experience” myths

And started optimizing for: 
✔ decision-making stories
✔ business judgment
✔ proof that someone would trust me in the room

That shift changed everything.

Global hiring isn’t harder.
It’s just calibrated differently.

If you’re preparing for roles beyond your home country, ask yourself: Are you proving skills…
or earning confidence?

Because globally, confidence hires before competence.

Here’s the uncomfortable truth most candidates don’t hear early enough:

Most data portfolios aren’t evaluated as “work.”
They’re evaluated as signals.

And most of them send the wrong ones.

I’ve reviewed a good number of portfolios as part of the hiring team.
The pattern is consistent:

What candidates show:
• Dashboards with no business question
• Kaggle-style analyses disconnected from real decisions
• Tools-heavy walkthroughs (“I used SQL, Python, Tableau…”)
• Perfect charts that solve imaginary problems

What hiring managers are actually scanning for:
• Can this person frame a messy business problem?
• Do they understand tradeoffs, not just techniques?
• Can they explain why something mattered — not just how they built it?
• Would I trust them to influence a decision with imperfect data?

Here’s the part most people miss:

A portfolio isn’t proof of skill.
It’s evidence of judgment.


That’s why two candidates can use the same dataset —
and only one gets a callback.

The one who: 
• Anchors the project to a real stakeholder decision
• Explains assumptions and constraints
• Shows what they didn’t do and why
• Communicates impact in plain business language

Strategic shift to make today:

Stop asking, “How do I show more tools?”
Start asking, “What decision does this help someone make?”

That question alone will put your portfolio ahead of 90% of applicants.

If you’re building a portfolio right now, build it like you already have the job — not like you’re trying to pass a course.

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