IT Leaders Directory: The Voices Shaping Modern Data & AI
The data and AI landscape moves fast. New frameworks appear every quarter. Cloud platforms evolve constantly. Best practices shift as technology matures.
But some voices cut through the noise. These are the people building the platforms we use daily, creating the standards we follow, and sharing knowledge that shapes how thousands of engineers work.
This directory collects those voices. The innovators, practitioners, and thought leaders who are actively defining what modern data engineering, machine learning, and AI development look like today.
Whether you’re looking to learn from the best, find mentors to follow, or understand who’s driving change in specific areas, this collection gives you a starting point.
AI & ML Visionaries
These leaders are pushing the boundaries of artificial intelligence and machine learning. They’re not just building models but rethinking how we approach intelligence, reasoning, and automation.
Andrew Ng
Co-founder of Coursera, founder of DeepLearning.AI, and former head of Baidu AI Group and Google Brain. Andrew has trained millions of people in machine learning fundamentals and continues to advocate for practical AI education and responsible deployment.
Yann LeCun
Chief AI Scientist at Meta and Turing Award winner. Pioneer of convolutional neural networks and one of the founding figures of deep learning. His work on computer vision laid the groundwork for modern image recognition systems.
Fei-Fei Li
Co-director of Stanford’s Human-Centered AI Institute. Created ImageNet, the dataset that revolutionized computer vision research. Strong advocate for ethical AI and diversity in technology.
Demis Hassabis
CEO and co-founder of Google DeepMind. Led the teams behind AlphaGo, AlphaFold, and other groundbreaking AI systems. Pushing AI toward solving fundamental scientific problems.
Andrej Karpathy
Former Director of AI at Tesla, founding member of OpenAI. Known for making complex AI concepts accessible through clear explanations and practical code examples. Created the popular CS231n course at Stanford.
Jeremy Howard
Co-founder of fast.ai and Kaggle competitions Grandmaster. Making deep learning accessible through practical courses and the fastai library. Strong believer in making AI education free and approachable.
Sam Altman
CEO of OpenAI. Leading the development and deployment of GPT models and ChatGPT. Vocal about both the potential and risks of advanced AI systems.
Daphne Koller
Co-founder of Coursera, founder of insitro. Pioneer in probabilistic graphical models and computational biology. Applying machine learning to drug discovery and personalized medicine.
Data Engineering Pioneers
These engineers are defining how we build, scale, and maintain data infrastructure. They created the tools and patterns that power modern data stacks.
Maxime Beauchemin
Creator of Apache Airflow and Apache Superset. Founded Preset to bring modern data visualization to more teams. His work on workflow orchestration changed how data teams operate.
Reynold Xin
Co-founder of Databricks and Apache Spark committer. Core contributor to Spark SQL and Delta Lake. Building the lakehouse architecture that many enterprises now rely on.
Jay Kreps
Co-creator of Apache Kafka, co-founder and CEO of Confluent. Kafka transformed how companies handle real-time data streams and event-driven architectures.
Martin Kleppmann
Author of “Designing Data-Intensive Applications” and researcher at University of Cambridge. His book is the bible for understanding distributed systems, databases, and data processing patterns.
Tristan Handy
Founder and CEO of dbt Labs. Transformed analytics engineering by bringing software engineering practices to data transformation. Made version control and testing standard in analytics.
Clemens Vasters
Principal Architect at Microsoft Azure, messaging and eventing expert. Deep expertise in distributed systems, event streaming, and cloud architecture patterns.
Modern Stack Leaders
These people are shaping how we think about building and deploying modern data platforms and analytics stacks.
Benn Stancil
Co-founder and Chief Analytics Officer at Mode. Writes extensively about analytics engineering, metrics layers, and how data teams should organize and work.
Emilie Schario
Head of Data at Amplify Partners. Thought leader in analytics engineering and modern data stack adoption. Advocates for better collaboration between data and business teams.
Chad Sanderson
Product Manager focused on data quality and data contracts. Strong voice in the data observability and data mesh conversations. Writes about making data products more reliable.
Pedram Navid
Analytics engineer and educator. Created resources for learning dbt and analytics engineering. Focuses on practical patterns for building reliable data pipelines.
Anna Filippova
Director of Ecosystem at Aiven. Expert in open source community building and cloud data infrastructure. Focuses on how companies can contribute to and benefit from open source projects.
Platform Founders & CTOs
These technical leaders built the platforms and companies that power modern data infrastructure.
Snowflake Leadership (Benoit Dageville, Thierry Cruanes)
Co-founders of Snowflake. Reimagined the data warehouse for the cloud era with separation of compute and storage. Their architecture influenced how modern cloud databases are built.
Ali Ghodsi
Co-founder and CEO of Databricks. Leading the push toward unified analytics platforms and the lakehouse architecture. Making Spark more accessible to data scientists and analysts.
George Fraser
Founder and CEO of Fivetran. Built the standard for automated data integration. Made it possible for analysts to access data from hundreds of sources without engineering help.
Vinod Marur
Former CTO at Kyvos Insights, enterprise analytics architect. Deep experience in OLAP systems, big data analytics, and making BI tools work at massive scale.
Jordan Tigani
Co-creator of Google BigQuery. Built one of the first serverless data warehouses. Now focused on making database technology more efficient and sustainable.
Real-Time Systems Experts
These engineers specialize in streaming data, event processing, and systems that operate at millisecond latency.
Jay Kreps (also listed above)
Beyond Kafka, Jay writes extensively about stream processing, log-centric architectures, and event-driven design patterns.
Tyler Akidau
Principal Engineer at Google, Apache Beam committer. Author of “Streaming Systems” and pioneer of the Dataflow model. Defined fundamental concepts in stream processing.
Neha Narkhede
Co-creator of Apache Kafka, co-founder of Confluent. Expert in distributed systems and event streaming architecture. Strong voice in building real-time data infrastructure.
Gwen Shapira
Principal Technologist at Confluent, Apache Kafka committer. Writes and speaks extensively about Kafka architecture, stream processing patterns, and data integration.
Stephan Ewen
Co-founder of Apache Flink and Ververica (now part of Alibaba). Expert in stateful stream processing and real-time analytics at scale.
Data Quality & Observability Leaders
Data breaks. These people focus on making sure we know when, why, and how to fix it.
Barr Moses
CEO and co-founder of Monte Carlo. Coined the term “data downtime” and popularized data observability as a practice. Advocates for treating data infrastructure with the same rigor as software systems.
Lior Gavish
Co-founder and CTO of Monte Carlo. Built data reliability monitoring tools and writes about data quality engineering patterns.
Chad Sanderson (also listed above)
Strong advocate for data contracts and preventing data quality issues at the source rather than catching them downstream.
Nick Schrock
Creator of GraphQL and Dagster. Focused on bringing better developer experience and testing practices to data engineering. Made data pipeline testing a first-class concern.
Community Champions & Educators
These people make data and ML knowledge accessible. They teach, write, organize events, and build communities.
Vicki Boykis
Senior ML engineer and prolific writer. Explains complex ML and data concepts in plain language. Known for in-depth technical blog posts that actually teach rather than just promote.
Chip Huyen
Author of “Designing Machine Learning Systems” and educator. Writes about MLOps, production ML, and the gap between research and deployment. Makes ML engineering practical and actionable.
Eugene Yan
Senior Applied Scientist at Amazon. Writes extensively about applied ML, recommendation systems, and how to actually ship ML products. Curates resources for ML practitioners.
Seattle Data Guy (Ben Rogojan)
Data engineering consultant and educator. Creates content explaining data engineering concepts, career advice, and practical tutorials for aspiring data engineers.
Zach Wilson
Creator of EcZachly, data engineering educator. Builds practical courses and content focused on SQL, data modeling, and analytics engineering fundamentals.
Mikkel Dengsøe
Staff Machine Learning Engineer and writer. Focuses on practical ML engineering, tooling, and building data science teams. Co-creator of Hamilton, a micro-framework for dataflows.
Open Source Pioneers
These contributors created and maintain the tools that form the foundation of modern data infrastructure.
Wes McKinney
Creator of pandas and co-creator of Apache Arrow. His work on in-memory data structures changed how we process data in Python and across language boundaries.
Fernando Pérez
Creator of IPython and co-founder of Project Jupyter. Made interactive computing accessible and changed how data scientists work and share research.
Travis Oliphant
Creator of NumPy and SciPy, founder of Anaconda. Built the foundation of the Python scientific computing ecosystem.
Jake VanderPlas
Director of Open Software at University of Washington, core contributor to Python scientific stack. Author of “Python Data Science Handbook” and maintainer of key visualization libraries.
Hadley Wickham
Chief Scientist at Posit (formerly RStudio). Creator of ggplot2, dplyr, tidyverse, and many other R packages. Transformed how R is used for data analysis and made tidy data principles standard.
MLOps & Production ML Leaders
Getting models into production and keeping them there requires different skills. These people define MLOps best practices.
Chip Huyen (also listed above)
Her book and writing focus heavily on production ML systems and the infrastructure needed to deploy models reliably.
Luigi Patruno
Former MLE at Netflix, founder of MLOps Community. Focused on building production ML platforms and sharing operational knowledge across companies.
Laszlo Sragner
Founder of MLOps Community and Hypergolic. Organizes learning resources and events for ML engineers focused on deployment and operations.
Goku Mohandas
ML Lead at Anyscale, creator of Made With ML. Teaches end-to-end ML development from training to deployment with practical, production-focused examples.
Jacopo Tagliabue
Lead AI Scientist at Coveo. Writes about real-world ML, recommendation systems, and the messy reality of shipping ML products in industry.
Cloud Architecture Experts
These architects understand how to build scalable, reliable systems on cloud platforms.
Adrian Cockcroft
Former VP of Cloud Architecture at AWS, previously at Netflix. Pioneered cloud-native architecture patterns and microservices design. Strong voice in observability and chaos engineering.
Kelsey Hightower
Former Staff Developer Advocate at Google Cloud. Made Kubernetes accessible and advocated for simpler, more maintainable cloud architectures. Known for clear communication about complex systems.
Corey Quinn
Chief Cloud Economist at The Duckbill Group. Expert in AWS cost optimization and cloud economics. Brings humor and clarity to cloud billing complexity.
Werner Vogels
CTO of Amazon. Architect of many AWS services. Writes and speaks about distributed systems, scalability, and building for failure.
Analytics & BI Innovators
These leaders are rethinking how we analyze data, build metrics, and make insights accessible across organizations.
Benn Stancil (also listed above)
Writes some of the most thoughtful pieces on where analytics is heading and how data teams should evolve.
Caitlin Moorman
VP of Customer Experience at dbt Labs. Advocates for analytics engineering as a discipline and better processes for managing data transformations.
Robert Yi
Head of Marketing at dbt Labs, formerly analytics engineer. Strong voice in the Modern Data Stack conversation and how data teams should organize their work.
Randy Au
Quantitative UX researcher at Google, data science writer. Focuses on the practical reality of working with data and building analytics that people actually use.
Data Governance & Ethics
As data systems grow, so do questions about privacy, bias, and responsible use. These voices lead those conversations.
DJ Patil
Former US Chief Data Scientist, VP of Product at RelateIQ (acquired by Salesforce). Advocates for responsible data use and understanding societal impacts of AI systems.
Cathy O’Neil
Author of “Weapons of Math Destruction” and founder of ORCAA. Highlights how algorithms can perpetuate bias and harm. Pushes for algorithmic accountability.
Timnit Gebru
Founder of Distributed AI Research Institute (DAIR). Researcher focused on AI ethics, bias in machine learning, and diversity in AI. Strong advocate for examining power structures in AI development.
Kate Crawford
Senior Principal Researcher at Microsoft Research, author of “Atlas of AI”. Examines social and political implications of AI systems. Research spans from labor practices to environmental costs of AI.
Rumman Chowdhury
Responsible AI lead, founder of Humane Intelligence. Works on algorithmic bias, AI ethics frameworks, and making fairness testing practical for organizations.
Database & Storage Innovators
The people building the systems that store and retrieve our data efficiently.
Andy Pavlo
Professor at Carnegie Mellon, database systems researcher. Maintains comprehensive resources on database research and emerging database technologies. Co-creator of Peloton and NoisePage databases.
Michael Stonebraker
Turing Award winner, creator of Ingres and Postgres. Founded multiple database companies including Vertica and VoltDB. His work shaped modern relational and analytical databases.
Pat Helland
Veteran of Microsoft, Amazon, and Salesforce. Deep expertise in distributed systems, transactions, and database architecture. Known for papers on eventual consistency and building for failure.
Selina Zhang
Engineering leader at Google working on distributed storage systems. Expertise in building reliable, scalable storage infrastructure.
Why Follow These Leaders?
The data field moves too fast for any one person to keep up. Following the right voices helps you:
Stay current on emerging tools and patterns without drowning in vendor marketing.
Learn from people who’ve solved problems at scale that you’re just encountering.
Understand trade-offs between different approaches rather than just following trends.
Build intuition about where the industry is heading and what skills matter.
Connect with communities of practitioners solving similar problems.
Most of these leaders share their knowledge freely through blogs, talks, open source contributions, and social media. They publish real experiences, not just theory.
How to Use This Directory
For Learning
Pick 3-5 people in areas you want to grow. Follow their blogs, watch their talks, read their papers. You’ll learn more from their shared experiences than from most courses.
For Career Development
Understand what leaders in your target role are focused on. If you want to be an ML engineer, see what Chip Huyen and Eugene Yan write about. If you’re moving toward data architecture, follow Martin Kleppmann and Pat Helland.
For Hiring & Team Building
When building a data team, understanding these voices helps you evaluate candidates and set technical direction. Are they following industry leaders? Do they understand current debates and trade-offs?
For Technology Decisions
Before adopting a new tool or pattern, see what experienced practitioners say about it. These leaders often share both successes and failures, giving you realistic expectations.
Contributing to This List
This directory will evolve. Technology changes, new voices emerge, and different perspectives become important.
If you think someone should be added, consider whether they:
Have made significant technical contributions to data, ML, or AI infrastructure.
Share knowledge that helps practitioners do their jobs better.
Have built tools, written influential papers, or created educational resources that shaped the field.
Represent perspectives or areas not well covered in this list.
The goal isn’t comprehensiveness but usefulness. These should be voices worth following, not just names worth knowing.
Final Thoughts
The people in this directory didn’t get here by following trends. They created them. They saw problems others ignored, built tools when none existed, and shared what they learned openly.
You won’t agree with everything they say. Some advocate for approaches that contradict others. That’s the point. The best learning comes from understanding different perspectives and making informed choices for your context.
Start with a few names in areas you care about most. Read what they write. Try what they suggest. Build your own judgment about what works.
The field needs more voices, not just more followers. Learn from these leaders, but don’t just copy them. Take what works, adapt it, and share what you learn.
Tags:
#DataEngineering #MachineLearning #AI #DataScience #MLOps #TechLeaders #DataStrategy #OpenSource #CloudArchitecture #DataQuality #DataGovernance #Analytics #RealTimeSystems #ThoughtLeadership #DataCommunity #BigData #DataPlatforms #SoftwareEngineering #DataInfrastructure #MLEngineering
