Kevin Niparko

Kevin Niparko is the VP of Product @ Twilio. Kevin joined Twilio through the acquisition of Segment where he spent an incredible 8 years in numerous different roles including as Head of Product. Before entering the world of product, Kevin was a Management Associate at the world-renowned, Bridgewater Associates. In Today's Episode with Kevin Niparko We Discuss: 1. From Bridgewater to Head of Product: How Kevin made his way from the world of asset management and analytics to leading product teams? What are 1-2 of Kevin's biggest takeaways from his time at Bridgewater with Ray Dalio? How did the 8 year journey with Segment leading to their $3BN acquisition impact his approach to product? 2. What Makes a Great Product Person: Does Kevin believe that product is more art or science? If he were to put a number on it? What would it be out of 100? Why does Kevin believe that all product people should learn to write? Why does Kevin believe that the best product people are generalists and not specialists? Why does Kevin think that analytics is an insanely good start for product people? 3. How to Hire the Best Product People: How does Kevin approach the hiring process for product hires today? What are the non-obvious traits of hires he looks for? How does he test for them? Does Kevin use case studies? Where do many fall down? What do the best do? 4. Product Reviews: Good vs Great: How often does Kevin do product reviews? Who is invited? How have product reviews changed in a world where the company is now fully remote? What is the difference between good and great product reviews? What is the single best product decision Kevin has made? What did he learn? What is the worst product decision Kevin made? How did that change his approach?

Kevin Niparko

VP of Product

TwilioSegment

Summary Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems Interview Introduction How did you get involved in the area of data management? Can you describe what Segment Unify is and the story behind it? What are the net-new capabilities that it brings to the Segment product suite? What are some of the categories of attributes that need to be managed in a prototypical customer profile? What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile? What is the potential impact of more detailed customer profiles on LTV? How do you manage permissions/auditability of updating or amending profile data? Can you describe how the Unify product is implemented? What are the technical challenges that you had to address while developing/launching this product? What is the workflow for a team who is adopting the Unify product? What are the other Segment products that need to be in use to take advantage of Unify? What are some of the most complex edge cases to address in identity resolution? How does reverse ETL factor into the enrichment process for profile data? What are some of the issues that you have to account for in synchronizing profiles across platforms/products? How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record? What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)? What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify? When is Segment Unify the wrong choice? What do you have planned for the future of Segment Unify? Contact Info Kevin LinkedIn Blog Hanhan LinkedIn 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 Machine Learning Podcast helps you go from idea to production with machine learning. 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. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Segment Unify Segment Podcast Episode Customer Data Platform (CDP) Golden Profile Reverse ETL MarTech Landscape The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener. Support Data Engineering Podcast

Kevin Niparko

VP of Product

TwilioSegment

Hanhan Wang

Segment