Intro

Summary
In this episode Praveen Gujar, Director of Product at LinkedIn, talks about the intricacies of product management for data and analytical platforms. Praveen shares his journey from Amazon to Twitter and now LinkedIn, highlighting his extensive experience in building data products and platforms, digital advertising, AI, and cloud services. He discusses the evolving role of product managers in data-centric environments, emphasizing the importance of clean, reliable, and compliant data. Praveen also delves into the challenges of building scalable data platforms, the need for organizational and cultural alignment, and the critical role of product managers in bridging the gap between engineering and business teams. He provides insights into the complexities of platformization, the significance of long-term planning, and the necessity of having a strong relationship with engineering teams. The episode concludes with Praveen offering advice for aspiring product managers and discussing the future of data management in the context of AI and regulatory compliance.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
• Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
• Your host is Tobias Macey and today I'm interviewing Praveen Gujar about product management for data and analytical platforms

Interview

• Introduction
• How did you get involved in the area of data management?
• Product management is typically thought of as being oriented toward customer facing functionality and features. What is involved in being a product manager for data systems?
• Many data-oriented products that are customer facing require substantial technical capacity to serve those use cases. How does that influence the process of determining what features to provide/create?
• investment in technical capacity/platforms
• identifying groupings of features that can be served by a common platform investment
• managing organizational pressures between engineering, product, business, finance, etc.
• What are the most interesting, innovative, or unexpected ways that you have seen "Data Products & Platforms @ Big-tech" used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on "Building Data Products & Platforms for Big-tech"?
• When is "Data Products & Platforms @ Big-tech" the wrong choice?
• What do you have planned for the future of "Data Products & Platforms @ Big-tech"?

Contact Info

• LinkedIn
• Website

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.

Links

• DataHub
  • Podcast Episode
• RAG == Retrieval Augmented Generation

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA