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

There are only 3 levels in SQL.

It took me years as a data analyst to reach the last one.

(you're gonna wanna save this 🏷️)

Here are all the skills you need to know in each level: 

Level 1: Newbie 
(the basic syntax) 
↳ select
↳ from
↳ where
↳ order by 
↳ group by 
↳ aggregate functions
↳ NULLs
↳ basic joins
↳ string manipulation 
↳ date manipulation
↳ arithmetic operators 

Level 2: False Confidence
(intermediate skills)
↳ CTEs
↳ subqueries
↳ set operations
↳ self joins
↳ window functions
↳ views & temp tables 
↳ case
↳ optimization
↳ pivoting & unpivoting 

Level 3: Reality sets in 
(advanced business problems)
↳ recursive CTEs
↳ indexing
↳ data modeling
↳ dealing with stakeholders

Level 4: Dunning-Kruger effect
(imposter syndrome all over again)
↳ realizing skills don’t matter and you still don’t know what you’re doing

♻️ repost this to your network and save this for later!

Jess Ramos

⚡️ Data Science & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

LinkedIn’s job match feature that helps you strategically focus your job search on all the right roles. Instead of mass applying for ALL the roles you can find, you can now apply for the ones that match your skills and experience the best.

This means that you’re optimizing your job search since you’re applying for roles that you’re MOST qualified for. In this challenging and competitive job market, you truly have to be strategic, data-driven, and work smarter— not harder!

LinkedIn is getting a major upgrade since they’re not JUST recommending roles to you anymore— they’re also telling you how good of a fit it is for you!!

I’m so excited to be partnering with LinkedIn on this since it’s been the biggest platform for my career growth over the past few years 💙

With LinkedIn dare to discover where you best fit with job match: linkedin.com/jobs
#ad #LinkedInPartner

Brand partnership • ⚡️ Data Science & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

I'll save you about $1M in consulting fees & 12 months of disappointment:

Your data strategy IS your AI strategy.

Most AI transformations are failing not because of the models and technology but because of what's upstream: the data.

Over the years it's been a never-ending cycle of promises:

↳ Machine learning was going to change everything. 
↳ AI was going to change everything. 
↳ Generative AI was going to change everything.
↳ Now Agentic AI is going to change everything.

The technology is never the problem.

Most organizations are sitting on databases full of duplicates, missing fields, undocumented schemas, and business logic that lives entirely inside one person's head who left the company in 2019. 

And that's exactly what you're feeding into these models. And now we're enabling agents to act autonomously at scale with confidence.

Agent confidence is NOT human confidence.

And the most dangerous part is that non-technical (and even some technical stakeholders) don't understand that. They see the little verified check mark, and take it as absolute truth.

We have to fix what's upstream before we can accelerate what's downstream.

♻️ repost to give your network a laugh today!

37% of companies plan to replace entry level roles w/ AI.

But IBM is doing the opposite. 

They're tripling their entry-level roles due to limits of AI adoption.

They're thinking about the longterm strategy instead of the quick financial savings.

Mid-level and senior-level talent doesn't just magically appear later on if you haven't had entry-level roles learning and growing within your company. 

Not only that, but they're rewriting their roles to account for AI adoption and use so people focus more time on the things that are harder to replace with AI.

Turns out implementing AI enterprise-wide is harder than we thought.

⚡️ Data Science, & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

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

The $200K Analytics Engineer: Why This Role Didn't Exist 5 Years Ago (And Why You Need One)

Last week, a startup offered $200K for an Analytics Engineer. Five years ago, this title didn't exist. Now they're commanding senior engineer salaries.

I've watched this evolution from both sides—hiring them and training them. The shift isn't about hype. It's about a fundamental gap that nobody else could fill.

𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐚 $𝟐𝟎𝟎𝐊 𝐫𝐨𝐥𝐞:

Data Engineers built rock-solid pipelines but didn't understand business. Analysts knew the metrics but couldn't scale their SQL models to production-level quality. The gap between raw data and business value became a chasm.

Enter the Analytics Engineer: someone who thinks in both languages.

𝐖𝐡𝐲 𝐭𝐡𝐞𝐲'𝐫𝐞 𝐰𝐨𝐫𝐭𝐡 𝐞𝐯𝐞𝐫𝐲 𝐩𝐞𝐧𝐧𝐲:

𝟏. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐛𝐨𝐭𝐡 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬
→ Translate "increase conversion" into data models
→ Explain why that join explodes costs to executives
→ Bridge engineering and business without a translator

𝟐. 𝐓𝐡𝐞𝐲 𝐨𝐰𝐧 𝐭𝐡𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐥𝐚𝐲𝐞𝐫
→ Single source of truth for revenue, churn, CAC
→ Version-controlled definitions everyone trusts
→ No more "which dashboard is right?" debates

𝟑. 𝐓𝐡𝐞𝐲 𝐦𝐚𝐤𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝟏𝟎𝐱 𝐟𝐚𝐬𝐭𝐞𝐫
→ Pre-built data marts ready for analysis
→ Documentation that actually exists
→ Self-serve analytics that actually works

𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐢𝐧 𝟓 𝐲𝐞𝐚𝐫𝐬?

• dbt made transformation accessible to SQL users
• Cloud warehouses removed compute constraints  
• Git brought software engineering to analytics
• Companies realized bad metrics = bad decisions

𝐓𝐡𝐞 𝐬𝐤𝐢𝐥𝐥𝐬 𝐜𝐨𝐦𝐦𝐚𝐧𝐝𝐢𝐧𝐠 $𝟐𝟎𝟎𝐊:

→ Advanced SQL (CTEs, window functions, optimization)
→ Data modeling (Kimball, Data Vault, or strong opinions)
→ dbt + Git + CI/CD pipeline mastery
→ Business acumen to challenge metric definitions
→ Communication skills to train entire orgs

𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐞𝐜𝐫𝐞𝐭:

The best Analytics Engineers don't just build models. They prevent million-dollar mistakes and drive ROI by asking: "Are we measuring the right thing?"

I've seen them catch revenue leaks, identify broken attribution, and save companies from betting on vanity metrics.

𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: Your data stack doesn't need another engineer or analyst. It needs someone who makes both sides 10x more effective.

That's worth $200K.

Are you hiring Analytics Engineers, or still trying to make analysts scale?

#DataEngineering #AnalyticsEngineering #DataCareers

Andrew Madson

Your database connector is 𝘚𝘖𝘖𝘖𝘖 1992 😂

And it's costing you. Your analytical pipeline could be 21-38x faster.

The culprit? You're still using ODBC or JDBC for columnar workloads. Apache Arrow benchmarks show the difference is staggering—and it comes down to one thing: conversion tax.

𝐎𝐃𝐁𝐂 (𝟏𝟗𝟗𝟐)
→ Row-oriented C API
→ Built for BI dashboards and legacy systems
→ High overhead: forces row-to-column conversion
→ Works with: Excel, Tableau, Power BI, Crystal Reports

𝐉𝐃𝐁𝐂 (𝟏𝟗𝟗𝟕)
→ Row-oriented Java API
→ Built for transactional apps and Spring/Hibernate
→ High overhead: Java object creation per value
→ Works with: Spring Boot, Apache Spark, Kafka Connect

𝐀𝐃𝐁𝐂 (𝟐𝟎𝟐𝟑)
→ Columnar-first API built on Apache Arrow
→ Built for analytical workloads and modern data tools
→ Eliminates the conversion bottleneck entirely
→ Works with: DuckDB, Polars, pandas, PyArrow, DataFusion

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐬𝐩𝐞𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞?

ODBC and JDBC were designed when row-based processing made sense. But analytical queries scan columns, not rows. Every time you pull data through ODBC/JDBC into a columnar tool, you're paying a conversion tax—serializing rows, deserializing into columns, over and over.

ADBC skips that entirely. Data stays in Arrow format from database to DataFrame. Zero conversion. Zero wasted cycles.

𝐑𝐮𝐥𝐞 𝐨𝐟 𝐭𝐡𝐮𝐦𝐛:
→ Transactional workloads? ODBC/JDBC still makes sense
→ Analytical pipelines with columnar data? ADBC is the move

𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: If you're running Pandas or Polars queries through JDBC, you're leaving serious performance on the table. That's why I am excited about companies like Columnar, which are making ADBC stable and more widespread.

Still on JDBC for analytics—what's holding you back from switching?

#DataEngineering #ApacheArrow #Analytics

FAANG got you hired in 2015. MANDO will get you hired in 2026.

Meta AI · Anthropic · NVIDIA · DeepMind · OpenAI

These five companies are building the models, the chips, and the infrastructure behind everything happening in AI right now. And they're teaching you what they know — for free!

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐟𝐫𝐞𝐞 𝐜𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐫𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠:

🔵 𝐌𝐞𝐭𝐚 𝐀𝐈 → Free Llama courses on Coursera. Full professional certificate on building generative AI apps — prompt engineering, fine-tuning, RAG, deployment.
🔗 https://lnkd.in/gCpPKUkB

🟠 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 → Anthropic Academy. Courses on their API, Model Context Protocol (MCP), Claude Code, AI Fluency, plus cloud deployment on AWS Bedrock and Google Vertex. Certificates included.
🔗 anthropic.skilljar.com

🟢 𝐍𝐕𝐈𝐃𝐈𝐀 → Deep Learning Institute (DLI). Dozens of free self-paced courses — generative AI, CUDA programming, RAG pipelines, robotics — with hands-on GPU-powered labs.
🔗 https://lnkd.in/gRtXsxrk

🔴 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐞𝐞𝐩𝐌𝐢𝐧𝐝 → AI Research Foundations on Google Skills. University-level curriculum on building and fine-tuning language models from scratch. Transformers, modern architectures, hands-on activities.
🔗 https://lnkd.in/g4H97fzd

⚪ 𝐎𝐩𝐞𝐧𝐀𝐈 → OpenAI Academy. Workshops, webinars, and on-demand content — from AI basics to advanced integration for engineers. Certifications rolling out with pilot programs at Walmart, John Deere, BCG, and major universities.
🔗 academy.openai.com

The knowledge gap in AI is real. But the access gap? These companies just closed it.

Which one are you starting with?

#AI #MachineLearning #CareerDevelopment

Data + AI Executive | 250K+ Community Builder | O’Reilly Author | Open Source Advocate | andrewmadson.com

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The Battle

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