Who should I follow to learn about Data engineering from top experts?

If you’re learning **data engineering from scratch**, I’d avoid following 30 different creators. Pick a small group, each for a specific purpose. ### My top picks - **Benjamin Rogojan (Seattle Data Guy)** - Probably my #1 recommendation for getting a feel for the *actual job*. Great for career advice, data architecture, pipelines, cloud, and what data engineers do day-to-day. He also has a structured **100-day data engineering guide**. [Start your data engineering journey with 100 Days Guide | Seattle Data Guy posted on the topic | LinkedIn](https://www.linkedin.com/posts/seattle-data-guy_100daysofdataengineering-activity-7397289629751656448-8UIK?utm_source=chatgpt.com) - **Zach Wilson** - Excellent for a practical roadmap and understanding the modern data stack. His recommended progression is roughly **SQL → Python → orchestration → Spark/cloud/data platforms**. [The 2025 Data Engineering Roadmap](https://lilys.ai/en/notes/platform-shift-20251219/2025-data-engineering-roadmap?utm_source=chatgpt.com) - **Andreas Kretz** - Follow him when you want to understand *why* systems are designed a certain way rather than simply learning another tool. He's particularly useful for architecture and production thinking. [Top 7 YouTube Channels To Learn Data Engineering](https://www.guvi.in/blog/top-youtube-channels-to-learn-data-engineering/?utm_source=chatgpt.com) - **Joseph Machado** - Very good for practical explanations of data engineering concepts and building things yourself. [2026 Data Engineering Roadmap ·](https://russellchubb.com/posts/what_does_a_data_engineer_need_to_know/?utm_source=chatgpt.com) - **Alexey Grigorev** - Follow for structured learning and hands-on projects. DataTalksClub is particularly valuable if you want a community and actual project-based learning. [2026 Data Engineering Roadmap ·](https://russellchubb.com/posts/what_does_a_data_engineer_need_to_know/?utm_source=chatgpt.com) - **Darshil Parmar** - Strong choice for end-to-end projects, particularly cloud/data-stack projects. [Top 7 YouTube Channels To Learn Data Engineering](https://www.guvi.in/blog/top-youtube-channels-to-learn-data-engineering/?utm_source=chatgpt.com) ### If I were starting today I'd follow **these five**: 1. **Seattle Data Guy** → career + big picture 2. **Data with Zach** → roadmap + modern data engineering 3. **Andreas Kretz** → architecture 4. **Darshil Parmar** → projects 5. **DataTalksClub** → structured hands-on learning Then learn in this order: **SQL → Python → databases/data modeling → ETL/ELT → Airflow/orchestration → cloud → Spark → data warehouses/lakehouses → system design** That order is more important than constantly chasing new tools. Recent 2026 roadmaps similarly emphasize building an understanding of **data systems and architecture**, rather than simply collecting technologies. [2026 Data Engineering Roadmap ·](https://russellchubb.com/posts/what_does_a_data_engineer_need_to_know/?utm_source=chatgpt.com) If you tell me **your current level (beginner / SQL analyst / software engineer / etc.) and your goal (job, promotion, or just learning)**, I can give you a **3-month data engineering learning roadmap with exactly who to follow and what to watch/read each week**.

**Follow these experts to learn Data Engineering: Jay Kreps (Kafka), Ali Ghodsi (Databricks), Neha Narkhede (Kafka/Confluent), Martin Kleppmann (distributed systems), Maxime Beauchemin (Airflow/dbt), and Wes McKinney (pandas/Arrow). They are widely recognized leaders shaping modern data infrastructure and tools.** ## 🌟 Key Data Engineering Leaders to Follow Expert| Known For| Why Follow Them ---|---|--- **Jay Kreps**| Co-creator of **Apache Kafka**, CEO of Confluent| Learn about event streaming and large-scale data pipelines.[Top 30 Data Engineering & Data Infrastructure Leaders [2026] - DigitalDefynd Education](https://digitaldefynd.com/IQ/famous-data-engineers/) **Neha Narkhede**| Kafka co-creator, CEO of Oscilar| Insights on streaming data and AI-driven risk systems.[Top 30 Data Engineering & Data Infrastructure Leaders [2026] - DigitalDefynd Education](https://digitaldefynd.com/IQ/famous-data-engineers/) **Ali Ghodsi**| Co-founder & CEO of **Databricks** , creator of Spark| Visionary behind the **Lakehouse architecture**; active in open-source.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Matei Zaharia**| Creator of **Apache Spark**, CTO of Databricks| Deep expertise in distributed computing, MLflow, and Delta Lake.[Top 30 Data Engineering & Data Infrastructure Leaders [2026] - DigitalDefynd Education](https://digitaldefynd.com/IQ/famous-data-engineers/) **Martin Kleppmann**| Author of _Designing Data-Intensive Applications_| Clear explanations of distributed systems and databases; academic \+ industry perspective.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Maxime Beauchemin**| Creator of **Apache Airflow** & Superset| Practical tools for orchestration and analytics; strong community presence.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Wes McKinney**| Creator of **pandas** and Apache Arrow| Essential for data manipulation and performance optimization.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Zhamak Dehghani**| Pioneer of **Data Mesh** architecture| Learn modern decentralized approaches to scaling data.[Top Data Engineering & Data Analytics Experts (2026)](https://echoglobal.tech/technologies/data/) **Ben Rogojan ("Seattle Data Guy")**| YouTube & blog tutorials| Accessible, practical content for beginners and practitioners.[People of Data Engineering](https://www.ssp.sh/brain/people-of-data-engineering/) ## 📌 How to Engage With Them * **Books & Blogs**: Martin Kleppmann’s _Designing Data-Intensive Applications_ is a must-read. Maxime Beauchemin and Benn Stancil publish thought-provoking blogs. * **Talks & Conferences**: Ali Ghodsi and Jay Kreps often keynote at data conferences (Databricks Summit, Kafka Summit). * **Social Media**: Many are active on LinkedIn and X (Twitter), sharing insights and debates on data infrastructure trends. * **Open Source Projects**: Contribute or follow development in Apache Kafka, Spark, Airflow, Arrow, and dbt to learn from their ecosystems. ## ⚠️ Risks & Challenges * **Overwhelming Content**: These leaders produce highly technical material; beginners may need structured learning (courses, bootcamps) before diving into their advanced talks. * **Bias Toward Their Tools**: Founders naturally promote their own platforms (Databricks, Confluent, etc.). Balance by following independent educators like Andreas Kretz (_Data Engineering Cookbook_) or Joe Reis (_Fundamentals of Data Engineering_).[People of Data Engineering](https://www.ssp.sh/brain/people-of-data-engineering/) * **Rapidly Changing Landscape**: Data engineering evolves quickly (e.g., rise of Data Mesh, lakehouse). Stay adaptable and cross-check multiple sources. 👉 Would you like me to curate a **beginner-friendly learning path** (books, courses, YouTube channels) alongside these experts, so you can build a solid foundation before diving into their advanced material?

Learning data engineering requires a mix of foundational theory, architectural design patterns, and hands-on tool experience. Following industry practitioners, educators, and thought leaders who share real-world insights, newsletters, and practical tutorials is one of the best ways to stay ahead. [How should I “properly learn” about Data Engineering as a beginner?](https://www.reddit.com/r/dataengineering/comments/1mb6wqc/how_should_i_properly_learn_about_data/#:~:text=Books%20like%20Fundamentals%20of%20Data%20Engineering%20or,and%20runs%20some%20transformation%20on%20a%20schedule.) ### **Top Data Engineering Experts & Creators to Follow** * **Zach Wilson (DataExpert.io)** [Recommended by Matt Martin's Substack](https://performancede.substack.com/recommendations#:~:text=He%20has%20had%20a%20lot%20of%20learnings,really%20like%20joe's%20take%20on%20data%20engineering.) * _Where to follow:_ LinkedIn, Substack, YouTube * _Why:_ Zach is widely known for practical career advice, bootcamps, and deep technical breakdowns of what it takes to land and succeed in data engineering roles at scale. * **Ben Rogojan (The Seattle Data Guy)** * _Where to follow:_ LinkedIn, YouTube, Substack * _Why:_ Excellent for understanding the "modern data stack," business context, tool evaluations, and high-level architecture. He frequently interviews other data leaders. * **Joe Reis** [Recommended by Matt Martin's Substack](https://performancede.substack.com/recommendations#:~:text=He%20has%20had%20a%20lot%20of%20learnings,really%20like%20joe's%20take%20on%20data%20engineering.) * _Where to follow:_ LinkedIn, Substack, Podcast (_The Data Stack Show_) * _Why:_ Co-author of _Fundamentals of Data Engineering_ (the definitive textbook for the field). Joe offers provocative, vendor-agnostic, and deeply philosophical takes on data architecture and business value. * **Tristan Handy** [5 Top Data Substacks 2024: Best in Data & Analytics Engineering](https://www.getorchestra.io/guides/5-top-data-substacks-2024-best-in-data-analytics-engineering#:~:text=Written%20by%20Tristan%20Handy%2C%20founder%20of%20dbt,using%20dbt%20to%20build%20robust%20data%20models.) * _Where to follow:_ LinkedIn, Substack (_The Analytics Engineering Roundup_) * _Why:_ Founder of dbt Labs. Essential if you want to understand **analytics engineering** , transformation workflows, and modern data modeling practices. [5 Top Data Substacks 2024: Best in Data & Analytics Engineering](https://www.getorchestra.io/guides/5-top-data-substacks-2024-best-in-data-analytics-engineering#:~:text=Written%20by%20Tristan%20Handy%2C%20founder%20of%20dbt,using%20dbt%20to%20build%20robust%20data%20models.) * **Joseph Machado (Start Data Engineering)** [Data Engineering Blogs & Newsletters](https://www.ssp.sh/brain/data-engineering-blogs-and-newsletters/#:~:text=*%20Start%20Data%20Engineering%20by%20Joseph%20Machado.,*%20Data%20People%20Etc.%20by%20Stephen%20Bailey.) * _Where to follow:_ Substack, YouTube, Website * _Why:_ Perfect for absolute beginners. He focuses on step-by-step projects, portfolio building, and teaching the fundamentals of orchestrating data pipelines. ### **Essential Newsletters & Curated Resources** If you prefer curated content delivered to your inbox to keep up with industry tools and trends, subscribe to these: * **Data Engineering Weekly** (by Ananth Packkildurai / Srivatsan Ramanujam): A fantastic round-up of the week’s best tutorials, open-source projects, and articles. [5 Top Data Substacks 2024: Best in Data & Analytics Engineering](https://www.getorchestra.io/guides/5-top-data-substacks-2024-best-in-data-analytics-engineering#:~:text=Why%20Follow%3A%20For%20data%20engineers%20and%20analysts,insights%20from%20across%20the%20data%20engineering%20community.) * **Data Elixir** (by Hugo Loubser): A weekly curation covering data engineering, data science, and infrastructure tools. [5 Top Data Substacks 2024: Best in Data & Analytics Engineering](https://www.getorchestra.io/guides/5-top-data-substacks-2024-best-in-data-analytics-engineering#:~:text=Each%20edition%20is%20packed%20with%20resources%20covering,SQL%20%26%20BI%20Weekly%20by%20Alan%20Duncan.) * **ByteByteGo** (by Alex Xu): While focused heavily on general system design, it is invaluable for data engineers who need to understand large-scale distributed systems, caching, and scalable architecture. [Substacks recommended by Data Engineer Things](https://dataengineerthings.substack.com/recommendations#:~:text=Recommended%20by%20Data%20Engineer%20Things%20*%20Data,*%20DataExpert.io%20Newsletter.%20By%20Zach%20Wilson.%20Subscribe.) ### **Must-Read Industry Engineering Blogs** Some of the best technical writing comes directly from engineering teams handling massive scale. Bookmark these: * **Netflix TechBlog** (Deep dives into streaming architectures, massive data platforms, and cloud infrastructure) * **Uber Engineering Blog** (Renowned for real-time data streaming architectures, Kafka usage, and petabyte-scale storage) * **Airbnb Tech Blog** (Great for learning about data quality frameworks, scheduling, and data democratization) [Data Engineering Blogs & Newsletters](https://www.ssp.sh/brain/data-engineering-blogs-and-newsletters/#:~:text=Table_title%3A%20%23%20Company%20Blogs%20Table_content%3A%20%7C%20Company,https%3A%2F%2Fwww.uber.com%2Fen%2DIN%2Fblog%2Fnew%2Ddelhi%2Fengineering%2F%20%7C%20%7C%20Yelp%20%7C%20https%3A%2F%2Fengineeringblog.yelp.com%2F%20%7C) ### **Recommended Core Reading** In addition to following creators, two books serve as the baseline curriculum for data engineers: 1. _Designing Data-Intensive Applications_ by Martin Kleppmann (The ultimate guide to understanding how storage, databases, and distributed data systems work under the hood). [How should I “properly learn” about Data Engineering as a beginner?](https://www.reddit.com/r/dataengineering/comments/1mb6wqc/how_should_i_properly_learn_about_data/#:~:text=Kleppmann's%20Designing%20Data%20Intensive%20Applications%20and%20Kimball's,build%20pipelines%20that%20scale%20and%20don't%20break.) 2. _Fundamentals of Data Engineering_ by Joe Reis and Matt Housley (A comprehensive look at the entire data lifecycle, from generation to consumption). What specific aspect of data engineering are you most interested in focusing on first (e.g., Python pipelines, cloud infrastructure, or data modeling)?

To fast-track your learning in data engineering, you should follow practitioners who actively share practical architectures, career advice, and deep dives into modern data stacks. Industry Thought Leaders & Authors 1. ** _Joe Reis_** – Co-author of _Fundamentals of Data Engineering_. He offers pragmatic, big-picture perspectives on data architecture, moving past hype to focus on what actually works. Check out his thoughts on Joe Reis Substack. [Data Engineering Blogs to Follow - by Daniel Beach](https://dataengineeringcentral.substack.com/p/data-engineering-blogs-to-follow) 2. ** _Ben Rogojan (The Seattle Data Guy)_** – Essential follow for practical data engineering, career advice, and ecosystem overviews. Read his insights on SeattleDataGuy Substack. [15 Data Engineering Newsletters to Stay Current - LinkedIn](https://www.linkedin.com/posts/sumonigupta_15-newsletters-every-data-engineer-should-activity-7493226715947859968-l1Mp) 3. **Zach Wilson** – Famous for bootcamps and no-nonsense advice on scaling pipelines, getting hired, and the realities of working as a data engineer at big tech. Follow his DataExpert.io Newsletter. [LinkedIn·Sumit Gupta 📊] 4. ** _Tristan Handy_** – CEO of dbt Labs and a pioneer in analytics engineering. Follow the Analytics Engineering Roundup for modern transformation workflows. [LinkedIn·Sumit Gupta 📊] 5. **Ananth Packkildurai** – Creator of Data Engineering Weekly, a brilliant curation of tools, trends, and tutorials. [LinkedIn·Sumit Gupta 📊] Engineering Blogs (For Deep Architecture) Instead of just individual social accounts, follow these corporate engineering blogs that publish masterclasses on handling massive scale: * **Netflix TechBlog** – Gold standard for distributed systems, data streaming, and cloud architecture. * **Uber Engineering Blog** – Incredible case studies on real-time data pipelines and massive data migrations. * **Airbnb Tech Blog** – Great for data modeling, data quality, and orchestration tools like Apache Airflow. Are you looking to focus more on **foundational concepts (SQL/Python/Modeling)** , **big data tools (Spark/Kafka)** , or **cloud/modern data stack (Snowflake/dbt)**? Let me know and I can tailor specific resources or a learning path for you.