Who should I follow to learn about Analytics Engineering?

If you want to **actually learn Analytics Engineering - not just follow tool announcements - I’d build a small “feed” of practitioners, educators, and communities.** ### My shortlist - **Tristan Handy** - Founder of dbt and one of the foundational voices behind Analytics Engineering. Follow him for the *why*: data team design, analytics development, and where the discipline is heading. His **Analytics Engineering Roundup** is particularly worth subscribing to. [The Analytics Engineering Roundup | Tristan Handy | Substack](https://roundup.getdbt.com/?utm_source=chatgpt.com) - **Claire Carroll** - Important if you want to understand the origins and philosophy of the Analytics Engineer role. Her work helped define the role as the bridge between analytics and software engineering. [Analytics Engineering: The Complete 2026 Guide · Analytics Engineering](https://www.analyticsengineering.com/guides/analytics-engineering?utm_source=chatgpt.com) - **Benn Stancil** - Excellent for developing judgment about analytics, data products, metrics, and how data teams should actually operate - not merely how to use dbt. - **Jason Ganz** - Good person to follow for the evolution of the data/analytics engineering community, particularly around the changing role of analytics engineers and AI. - **Daniel Poppy** - Especially relevant in 2026 because he writes about how AI is changing the Analytics Engineer's responsibilities around modeling, governance, context, and reliability. [The analytics engineer in 2026: system designer, governance owner, AI context provider | dbt Labs](https://www.getdbt.com/blog/the-analytics-engineer-in-2026-system-designer-governance-owner-ai-context-provider?utm_source=chatgpt.com) - **Katie Claiborne** - Worth following if you're interested in the engineering side: deployment, DevOps practices, infrastructure, and treating analytics as production software. [Coalesce 2024: Why analytics engineering and DevOps go hand-in-hand | dbt Labs](https://www.getdbt.com/resources/coalesce-on-demand/coalesce-2024-why-analytics-engineering-and-devops-go-hand-in-hand?utm_source=chatgpt.com) - **Eric Provencio** - A useful follow if you're approaching Analytics Engineering as a career; his material focuses heavily on what the job actually looks like and how to become employable. [Analytics Engineering: The Complete 2026 Guide · Analytics Engineering](https://www.analyticsengineering.com/guides/analytics-engineering?utm_source=chatgpt.com) ### Organizations/resources I'd follow - **[dbt Labs](https://www.getdbt.com/?utm_source=chatgpt.com)** - probably the single most important ecosystem to follow. Their current research shows that Analytics Engineering in 2026 is increasingly about **trust, governance, data quality, and AI-assisted development**, not simply writing dbt models. [2026 State of Analytics Engineering Report | dbt Labs](https://www.getdbt.com/resources/state-of-analytics-engineering-2026?utm_source=chatgpt.com) - **[Analytics Engineering Roundup](https://roundup.getdbt.com/?utm_source=chatgpt.com)** - my #1 recommendation for staying plugged into the field without doom-scrolling hundreds of accounts. - **[Analytics Engineering](https://www.analyticsengineering.com/?utm_source=chatgpt.com)** - newer, practical learning material covering SQL, dbt, modeling, warehouses, BI, and career development. [SQL, dbt & Analytics Engineering Guides · Analytics Engineering](https://www.analyticsengineering.com/resources?utm_source=chatgpt.com) - **[dbt Community](https://www.getdbt.com/community?utm_source=chatgpt.com)** - useful once you want to see real practitioners discussing problems rather than consuming tutorials. [Subscribe to dbt Labs' newsletter | Stay informed on data trends](https://www.getdbt.com/learn/newsletter?utm_source=chatgpt.com) ### If you're starting from zero I'd follow **Tristan Handy + Claire Carroll + Benn Stancil + Jason Ganz**, subscribe to the **Analytics Engineering Roundup**, and spend most of your actual learning time on: **SQL → data modeling → dbt → Git/software engineering → testing & documentation → warehouse fundamentals → BI/metrics → orchestration → governance → AI-assisted analytics engineering.** That progression is much more valuable than trying to follow every new data-tool influencer. And one important 2026-specific point: **don't learn Analytics Engineering as “the dbt job.”** dbt is central, but the underlying skill is turning messy business data into **trusted, maintainable, documented analytical systems**. [Analytics engineering: Six best practices for success | dbt Labs](https://www.getdbt.com/blog/analytics-engineering-six-best-practices?utm_source=chatgpt.com) If you tell me your current level (**analyst / data engineer / software engineer / beginner**) and whether you're aiming for an **Analytics Engineer job**, I can give you a **10-person follow list + 30-day learning plan** tailored to you.

**To learn about Analytics Engineering, follow leaders who bridge data engineering, analytics, and modern data stack practices - names like Tristan Handy (dbt Labs), Barr Moses (Monte Carlo), and George Fraser (Fivetran) are especially influential.** These experts consistently share insights on building reliable data pipelines, analytics workflows, and governance strategies.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/)[The Top 25 Data Management and Analytics Executives of 2025 - The AI & Software Report](https://theaisoftwarereport.com/the-top-25-data-management-and-analytics-executives-of-2025/) ## 🌟 Key Figures in Analytics Engineering Expert| Role / Affiliation| Why Follow Them ---|---|--- **Tristan Handy**| Founder of dbt Labs| Pioneer of the _analytics engineering_ movement; shares practical frameworks for transforming data with dbt.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Barr Moses**| CEO of Monte Carlo| Leading voice on _data reliability and observability_; posts about preventing broken dashboards and ensuring trustworthy analytics.[The Top 25 Data Management and Analytics Executives of 2025 - The AI & Software Report](https://theaisoftwarereport.com/the-top-25-data-management-and-analytics-executives-of-2025/) **George Fraser**| CEO of Fivetran| Focuses on _data integration and automation_; explains how to streamline pipelines for analytics teams.[The Top 25 Data Management and Analytics Executives of 2025 - The AI & Software Report](https://theaisoftwarereport.com/the-top-25-data-management-and-analytics-executives-of-2025/) **Wes McKinney**| Creator of pandas & Apache Arrow| Shares deep technical insights on _data processing tools_ that underpin analytics workflows.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Maxime Beauchemin**| Creator of Apache Airflow & Superset| Advocates for _data orchestration and visualization_; his work shapes how analytics engineers manage pipelines.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Cindi Howson**| Chief Data Strategy Officer at ThoughtSpot| Provides thought leadership on _BI and analytics adoption_ in enterprises.[The 2025 Global Top 100 Innovators in Data, Analytics and AI](https://www.coriniumintelligence.com/hubfs/COR_TOP100_2025_FINAL.pdf) **Ronald van Loon**| Analytics influencer| Shares accessible content on _big data, BI, and analytics trends_ for a broad audience.[Meet 17 of the Top Influencers in Big Data and Analytics in 2026](https://www.simplilearn.com/big-data-and-analytics-top-influencers-article) ## 📌 Why These Voices Matter * **Practical frameworks**: Handy and Beauchemin focus on tools (dbt, Airflow, Superset) that analytics engineers use daily. * **Reliability & trust**: Moses emphasizes observability - critical for ensuring analytics outputs are correct. * **Integration & scale**: Fraser and McKinney highlight how to move and process data efficiently. * **Strategic adoption**: Howson and van Loon help contextualize analytics engineering within business impact. ## ⚠️ Risks & Challenges * **Tool bias**: Many leaders are founders of specific platforms (dbt, Fivetran, Monte Carlo). Their advice can be product-centric. Balance by following multiple voices. * **Rapid evolution**: Analytics engineering is fast-moving; practices from 2024 may already be outdated. Always check for _latest posts, talks, or papers_. * **Overemphasis on tech**: Some influencers focus heavily on tools, less on organizational change. Complement with voices on _data culture and governance_. ## ✅ Next Steps * Follow Tristan Handy and Barr Moses on LinkedIn/Twitter for _hands-on analytics engineering insights_. * Subscribe to blogs/newsletters from dbt Labs, Monte Carlo, and Fivetran for _practical case studies_. * Balance technical voices (McKinney, Beauchemin) with strategic ones (Howson, van Loon) to get both _implementation and adoption perspectives_. Would you like me to curate a **starter list of social media handles, blogs, and newsletters** so you can follow these experts directly?

To learn Analytics Engineering effectively, you should follow industry practitioners, creators, and organizations that actively define best practices in data modeling, SQL transformation, and the Modern Data Stack. [Top Analytics Engineering Communities 2025](https://www.analyticsengineering.com/resources/best-analytics-engineering-communities-forums-to-join?utm_source=gemini#:~:text=Members%20gain%20access%20to%20active%20Slack%20channels,Job%20board%20focused%20on%20analytics%20engineering%20roles.) **Key Thought Leaders & Practitioners to Follow** * **Tristan Handy:** CEO and founder of dbt Labs; often considered a pioneer of the analytics engineering movement. Follow his writings and updates for macro trends in the data ecosystem. * **Benn Stancil:** Co-founder of Mode and a prominent writer on data culture, analytics tooling, and the evolving role of data professionals. * **Claire Carroll:** A leading voice in the dbt Community who focuses heavily on education, analytics engineering workflows, and community building. * **Nick Handel:** Co-founder of Castor and a great follow for insights on data cataloging, governance, and modern stack architectures. * **Data Stack Leaders & Engineers:** Look up practitioners on LinkedIn and X who frequently share tips on SQL optimization, dimensional modeling, and CI/CD for data (such as folks sharing insights from local _Locally Optimistic_ or _dbt Community_ circles). **Essential Communities & Platforms** * **The dbt Community:** The definitive hub for analytics engineering. Their Slack workspace has thousands of active practitioners sharing code snippets, architectural advice, and career tips. [Top Analytics Engineering Communities 2025](https://www.analyticsengineering.com/resources/best-analytics-engineering-communities-forums-to-join?utm_source=gemini#:~:text=dbt%20Community.%20The%20dbt%20Community%20serves%20as,Weekly%20office%20hours%20with%20dbt%20Labs%20team.) * **Locally Optimistic:** A vibrant community run by and for data practitioners, great for discussions on career growth, team structures, and data engineering culture. * **Substack Newsletters:** Subscribe to data-focused publications like _The Data Stack Show_ , _Benn.substack.com_ , and various analytics engineering newsletters that dissect real-world enterprise architectures. **Recommended Foundations to Study** * **The dbt Fundamentals Course:** A completely free, gold-standard starting point provided by dbt Labs to learn modern transformation workflows. [To all my Analytics Engineers here, how you made it and ... - Reddit](https://www.reddit.com/r/dataengineering/comments/1n1mmwk/to_all_my_analytics_engineers_here_how_you_made/?utm_source=gemini#:~:text=I've%20helped%20two%20friends%20get%20AE%20roles%2C,around%20putting%20theory%20into%20practice.%20good%20luck!) * **The Data Model Resource Book / Kimball Methodology:** Essential reading on dimensional modeling, star schemas, and grain - the underlying theory that separates good analytics engineers from basic SQL scripters. [To all my Analytics Engineers here, how you made it and ... - Reddit](https://www.reddit.com/r/dataengineering/comments/1n1mmwk/to_all_my_analytics_engineers_here_how_you_made/?utm_source=gemini#:~:text=I%20also%20recommend%20getting%20the%20fundamentals%20certification,explain%20your%20thought%20process%20well%20during%20interviews.) Are you looking to transition into analytics engineering from a background in data analysis or software engineering?

To truly master Analytics Engineering (AE), you want to follow practitioners who post actionable advice on data modeling, **dbt** , SQL, and modern data stack architecture. [6 dbt & Analytics Engineering Creators to Follow | Bruno Lima](https://www.linkedin.com/posts/brunoszdl_dbt-analyticsengineering-activity-7435039472590700545-LYpY)[This is how I'd learn dbt in 30 days and actually become an](https://www.instagram.com/reel/DUN8Wzlkl3o/) The top voices, creators, and community leaders to follow across LinkedIn and other platforms include: * * ** _Madison Schott_** - A prominent and active voice in the analytics engineering space, sharing great practical advice, career insights, and workflow patterns for scaling data teams. [LinkedIn·Bruno Lima] * **Juan Manuel Perafan** - Co-author of _Fundamentals of Analytics Engineering_ and a dbt Community Award winner. He hosts the _SQL Lingua Franca_ podcast, which is fantastic for deep-dive technical listening. [LinkedIn·Bruno Lima] * **David Uforo Effiong** - Known for high-quality, highly visual LinkedIn and YouTube breakdowns that make tricky AE and dbt concepts bite-sized and approachable. [LinkedIn·Bruno Lima] * ** _Andrew Madson_** - Head of DevRel at Fivetran, offering transparent, ecosystem-wide perspectives on transformation tools, data pipelines, and data stacks. [LinkedIn·Bruno Lima] * ** _Ben Rogojan (The Seattle Data Guy)_** - Essential for zooming out past just transformation to understand the broader data engineering and architecture landscape. [Top 22 Data Influencers to Follow in 2025 - Rivery](https://rivery.io/blog/best-data-influencers/) * **Zach Wilson** - Great for no-nonsense advice on data engineering, career growth, and how analytics engineering fits into big tech pipelines. [Top Data Engineering Influencers to Follow on LinkedIn | Dr Emmanuel Ogungbemi posted on the topic](https://www.linkedin.com/posts/eoogungbemi_top-data-engineering-creators-to-follow-on-activity-7431411488336101377-x6Ur) * Communities & Ecosystems to Follow Instead of just passive scrolling, the real gold standard for learning Analytics Engineering happens inside practitioner-heavy communities: * * **The dbt Slack Community** : Join the official [dbt Community](https://datatalks.club/blog/slack-communities.html) (with tens of thousands of analytics engineers). It is the absolute epicenter for troubleshooting SQL, architectural debates, and seeing how others model data. * **Locally Optimistic** : A fantastic, vendor-neutral [Slack community](https://data-storyteller.medium.com/list-of-data-analytics-online-communities-70831894aef7) run by and for data leaders and analytics engineers discussing strategy and operations. [20+ Best Data Science Slack Communities to Join in 2025](https://datatalks.club/blog/slack-communities.html)[List of Data & Analytics Online Communities](https://data-storyteller.medium.com/list-of-data-analytics-online-communities-70831894aef7) * Are you looking to learn the **fundamentals (SQL and data modeling)** , or are you trying to dive straight into **tools like dbt and the modern data stack**?