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Best 13 Spark Consultants for Scalable Data Solutions

spark consultants - Best 13 Spark Consultants for Scalable Data Solutions

Apache Spark has become the backbone of modern big data processing, powering everything from enterprise-scale ETL pipelines to cutting-edge machine learning and AI applications.

Behind its success is a global community of expert engineers, early contributors, prolific educators, and core committers who’ve shaped Spark’s evolution from a research project to a production-grade analytics powerhouse. This curated list highlights  the most influential Spark consultants and engineers active today—individuals known for creating key components, writing the most trusted books, leading high-impact data teams, or driving Spark adoption in industry and open source.

  1. Sean Owen
  2. Reynold Xin
  3. Jean-Georges Perrin
  4. Sandy Ryza
  5. Michael Armbrust
  6. Hyukjin Kwon
  7. Ram Sriharsha
  8. Denny Lee
  9. Holden Karau
  10. Jacek Laskowski
  11. Nick Pentreath
  12. Jason Dai
  13. Brooke Wenig

Now, let’s take a closer look at each of these experts and what makes them stand out:

Sean Owen

YouTube Video

Nationality: British

Sean is an Apache Spark PMC member and was one of the earliest evangelists for applying Spark in data science.

As Director of Data Science at Cloudera a decade ago, he championed Spark for scalable machine learning, creating tools like Oryx (a real-time recommender system on Spark). He later joined Databricks to lead its global data science practice, contributing to Spark’s MLlib library and community growth. Sean co-authored the book Advanced Analytics with Spark and has been a Kaggle competition master, blending practical machine learning with big-data tech. He currently works as a Staff Research Scientist at Databricks’ MosaicML division, pushing the boundaries of ML systems on Spark.

Reynold Xin

The big idea behind Spark is that distributed computing should be as easy as writing a Python script.

Nationality: Chinese

Reynold is a co-founder and Chief Architect at Databricks, best known for his influential work on Spark’s engine internals.

As an original Spark PMC member, he designed several core components of Spark, including GraphX (graph processing), Project Tungsten (memory optimization), Structured Streaming, and he co-led development of the DataFrame API. Reynold even led a team that won the Daytona GraySort big data sorting benchmark in 2014 using Spark, exemplifying Spark’s performance. Today, he continues to guide Spark’s technical evolution and is a key voice in the big data community worldwide.

Jean-Georges “jgp” Perrin

Jean Georges jgp Perrin - Best 13 Spark Consultants for Scalable Data Solutions

Nationality: French

Jean-Georges is the author of Spark in Action, 2nd Edition (Manning, 2020), a comprehensive book on building data engineering pipelines with Spark. An IBM Champion and veteran consultant, “jgp” has spent over 20 years bringing big-data solutions to enterprises.

He was among the first to introduce Apache Spark to companies in France, often speaking at conferences like Spark Summit and All Things Open to promote its adoption. After years running his own consulting practice, he joined PayPal in 2021 as an engineer focused on data mesh architecture, and in 2023 became Chief Innovation Officer at AbeaData, a data platform startup. Perrin’s deep experience with enterprise data integration and his ongoing advocacy for open-source make him a go-to expert in the Spark community.

Sandy Ryza

Nationality: American

Sandy co-authored Advanced Analytics with Spark (2015), one of the earliest books demonstrating real-world machine learning recipes using Spark.

As an early Spark committer at Cloudera, he worked on Spark’s MLlib library and its integration with Hadoop, and contributed to improvements in Spark’s scheduling and metrics systems. After Cloudera, Sandy led data science teams at Clover Health. He is now a Lead Engineer at Dagster Labs, building next-generation data orchestration tools for pipelines. With his broad experience across Spark, Hadoop, and modern data workflow management, Sandy remains a respected thought leader in data engineering. He frequently shares insights on how to design robust pipelines, bridging his past in Spark with his present work in orchestration.

Michael Armbrust

Michael Armbrust - Best 13 Spark Consultants for Scalable Data Solutions

Nationality: American

Michael is the mastermind behind Spark SQL – he created Spark’s DataFrame API and the Catalyst SQL optimizer, fundamentally expanding Spark beyond MapReduce into structured data processing. He later designed Structured Streaming and helped develop Delta Lake, making streaming and reliable data lakes a native part of Spark’s ecosystem. As an Engineering Director at Databricks and a Spark PMC member, Michael leads development of Spark’s core SQL engine and next-generation features. His contributions (like the Tungsten execution engine and Adaptive Query Execution) have made Spark a go-to unified analytics engine in industry. He frequently shares new Spark advancements at conferences and remains a driving force as Spark evolves toward version 4.x.

Hyukjin Kwon

Nationality: South Korean

Hyukjin is an Apache Spark PMC member and the lead engineer for PySpark APIs. He spearheaded the Koalas project (pandas API on Spark), which has since been merged into Spark to allow pandas users to scale their code seamlessly on Spark clusters.

Formerly a Staff Software Engineer at Databricks and now at Apple, Hyukjin focuses on bridging Python and Spark – optimizing pandas UDFs, Arrow integration, and overall PySpark performance. He’s also a frequent speaker at PyData, Spark+AI Summit and other conferences, where he shares best practices for large-scale data processing in Python. Hyukjin’s work has been crucial in making Spark more accessible to the Python data science community.

Ram Sriharsha

Ram Sriharsha - Best 13 Spark Consultants for Scalable Data Solutions

Nationality: Indian

Ram is an Apache Spark PMC member known for contributions to Spark’s MLlib machine learning library and runtime performance. At Yahoo (and later Hortonworks), he led efforts to integrate Spark with Hadoop and authored improvements in Spark’s memory management and ML pipelines.

Ram subsequently took on engineering leadership roles and most recently joined the vector database startup Pinecone, where he is now Chief Technology Officer. In his new role, he applies his distributed systems know-how to AI similarity search, reflecting how his career bridges Spark and the emerging AI/ML stack. He holds a Ph.D. in theoretical physics, giving him a strong analytical foundation, and is a frequent speaker on data platforms. Ram remains active in open source (contributing to projects like Apache Arrow) and uniquely versed in both big data processing and cutting-edge AI infrastructure.

Denny Lee

Nationality: American

Denny is a hands-on data engineer and Apache Spark contributor with 20+ years of experience. He is currently a Senior Staff Developer Advocate at Databricks, focusing on the open-source Delta Lake project and best practices for “lakehouse” data architecture.

Denny was part of Microsoft’s early big data team, where he helped bring Hadoop to Azure and co-founded the HDInsight Spark service. He co-authored Learning Spark, 2nd Edition and is a maintainer of Delta Lake. A long-time Seattleite, Denny shares his expertise through blogs, talks, and even podcasts, and is known for his approachable explanations of complex Spark topics (often peppered with his enthusiasm for cycling and coffee!). As an advocate, he’s helped countless engineers worldwide get up to speed with Spark and modern data engineering.

Holden Karau

Holden Karau - Best 13 Spark Consultants for Scalable Data Solutions

Nationality: Canadian

Holden is an open-source engineer and prolific author in the Spark community. A Spark committer since the project’s early days, she co-authored some of the most widely read books on Spark, including Learning Spark (O’Reilly, 2015) and High Performance Spark (2017).

Holden has worked at companies like IBM, Google, and Apple on big data platforms, and is known for her contributions to Spark’s Python APIs and testing infrastructure (she created the popular spark-testing-base library to simplify unit testing Spark code). Beyond coding, Holden is a frequent conference speaker and blogger, acclaimed for her fun and accessible presentations on complex Spark internals. She’s also an advocate for diversity in tech and open source. Currently, Holden is the founder of a startup using AI to help consumers (while still contributing to open source on the side), demonstrating that she remains passionate about solving real-world problems with Spark and AI.

Jacek Laskowski

Nationality: Polish

Jacek is a freelance consultant and technical instructor specializing in Apache Spark, Kafka, and related big data technologies. Widely regarded as a Spark guru, he is the author of the online books The Internals of Apache Spark and Mastering Apache Spark 2.x, which are go-to resources for developers seeking a deep understanding of Spark’s inner workings.

Jacek has been recognized as a Databricks Beacon (MVP) for his community contributions. He spends much of his time training engineering teams and writing detailed blog posts dissecting Spark components (from Spark SQL’s Catalyst optimizer to Structured Streaming). With over 20 years of IT experience, Jacek has helped numerous companies in Europe adopt and optimize Spark for their data pipelines. His enthusiasm for sharing knowledge and his hands-on approach to solving Spark problems have made him one of the top independent Spark consultants in the world.

Nick Pentreath

Nick Pentreath - Best 13 Spark Consultants for Scalable Data Solutions

Nationality: South African

Nick is a principal engineer and machine learning specialist who has been a prominent contributor to Apache Spark’s MLlib library. He is the author of Machine Learning with Spark (Packt, 2015), one of the first books to show how to build ML pipelines on Spark.

Nick was an early member of the Spark Technology Center at IBM, where he worked on advancing Spark’s machine learning capabilities and helped enterprises deploy Spark for AI solutions. He later joined the Apache Spark PMC, contributing code to MLlib and mentoring its growth. Nick’s expertise spans machine learning, recommendation systems, and deep learning integration with Spark. He is also an active open-source contributor beyond Spark (including projects in the Hadoop ecosystem and model deployment frameworks). Currently based in Cape Town and working with the Apache Software Foundation, Nick consults and advises companies globally on big data ML architecture. His combination of competition-level machine learning skills and real-world Spark experience puts him among the elite Spark consultants.

Jason Dai

Nationality: Chinese

Jason is a Senior Principal Engineer at Intel and one of the most influential Spark contributors in the intersection of big data and AI. He created BigDL, an open-source deep learning library for Spark, enabling distributed AI workloads on Spark clusters.

As Intel’s first-ever Engineering Fellow in China, Jason leads a global team pushing the boundaries of analytics and AI on unified platforms. He has contributed to Spark MLlib and is also a PMC member of Apache Spark. Jason frequently speaks at industry conferences and wrote authoritative articles on scaling deep learning with Spark (including work on Analytics-Zoo). In 2023, he was recognized for his leadership in developing Intel’s oneAPI AI analytics toolkit that integrates with Spark. Jason holds a Ph.D. and has extensive research publications, but he focuses on practical solutions – helping enterprises use Spark with BigDL for tasks like large-scale recommendation systems and image analytics. For companies looking to implement machine learning or deep learning at scale on Spark, Jason’s expertise is second to none.

Brooke Wenig

Nationality: American

Brooke is the Director of the Global Machine Learning Practice at Databricks, where she leads a team of data scientists and ML engineers in implementing large-scale ML pipelines for customers.

She joined Databricks in 2016 and quickly became a prominent instructor and advisor for how to use Apache Spark for machine learning use cases. Brooke is a co-author of Learning Spark, 2nd Edition and co-hosts the Data Brew podcast, in which she interviews experts on practical data and AI topics. She is an expert in MLOps on Spark, helping enterprises design reproducible and efficient model development workflows on top of Delta Lake and MLflow. Brooke is also an international speaker (often appearing as a keynote host at Data + AI Summits) and is recognized for her ability to explain complex concepts like deep learning, LLMs, and data governance to both technical and non-technical audiences.

With her strong combination of technical chops and real-world experience across numerous industries, Brooke stands out as a top Spark consultant for any organization looking to scale out machine learning and AI on a Spark platform.

Wrap Up

These legends represent exceptional talent, making them extremely challenging to headhunt. However, there are thousands of other highly skilled IT professionals available to hire with our help. Contact us, and we will be happy to discuss your hiring needs.

Note: We’ve dedicated significant time and effort to creating and verifying this curated list of top talent. However, if you believe a correction or addition is needed, feel free to reach out. We’ll gladly review and update the page.

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