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!

Andrew C. Madson

Data Analysis Top Voice | Professor | Product Management @ LPL Financial

I'm a Data & AI Evangelist, Keynote speaker, Opensource Advocate, and Professor.

I LOVE transforming the complex into the accessible. My insights combine my multiple master's degrees in data analytics and business management with hands-on leadership experience at enterprise organizations. 


My technical expertise spans data analytics, machine learning, and artificial intelligence, complemented by strong communication skills developed through roles as an adjunct instructor at six universities and keynote speaker. I bridge the gap between technical innovation and practical application, helping organizations unlock the full potential of their data.

DM to collaborate 🙌

AVP - Product Management - Compliance Analytics and Monitoring

So many data teams tell me: "We're not ready for Iceberg... we're not Netflix!"

Netflix didn't adopt Iceberg because they were Netflix. They adopted it because they had problems that Iceberg solves.

Guess what? You have those same problems.

𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐨𝐥𝐯𝐞𝐬:

→ Vendor lock-in bleeding your budget
→ Schema changes breaking downstream jobs
→ Scrambling when finance asks "what did this look like last week?"
→ Paying for compute and storage bundled together
→ Migrating between tools feeling impossible

These aren't Netflix-scale problems. These are Tuesday problems for a 3-person data team.

𝐖𝐡𝐚𝐭 𝐈'𝐯𝐞 𝐬𝐞𝐞𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝:

A 50-person startup moved to Iceberg. Not because they needed petabyte scale—they had 200GB. They needed to stop rewriting pipelines every time they switched tools.

A mid-size e-commerce company adopted it for one reason: their Snowflake bill was growing faster than revenue. Separating storage from compute changed that math overnight.

Neither of them was Netflix. Both of them had real problems that got solved.

𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬𝐧'𝐭 "𝐚𝐫𝐞 𝐰𝐞 𝐛𝐢𝐠 𝐞𝐧𝐨𝐮𝐠𝐡?"

It's: Are we frustrated enough with the current limitations?

If you're tired of being locked into one vendor's pricing model, if schema evolution keeps breaking things, if you want your data to outlive your current tooling decisions—you're ready.

𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: Iceberg isn't a scale play. It's a flexibility play. Flexibility matters more at 100GB than at 100PB. That's when your decisions compound.

What's actually holding your team back—readiness or unfamiliarity?

#DataEngineering #ApacheIceberg #DataArchitecture

Andrew Madson

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