Netflix's Data Engineers - An Interview with Kevin Wylie

This is the second instalment of our "Data Engineers of Netflix" series, in which our very own data engineers discuss their paths to Data Engineering at Netflix.

Kevin Wylie works on the Content Data Science and Engineering team as a Data Engineer. Kevin discusses his considerable expertise in content analytics at Netflix, which he joined more than a decade ago.

Kevin grew raised in the Washington, DC, region and graduated from Virginia Tech with a bachelor's degree in mathematics. Prior to joining Netflix, he worked at MySpace, where he assisted in the implementation of page categorization, pathing analysis, and sessionization, among other features. In his spare time, he enjoys gardening and sports with his four children.

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Ozark, Breaking Bad, Black Mirror, Barry, and Chernobyl are some of his favourite television shows.

My favourite project since joining Netflix in 2011 has been creating and building the first version of our entertainment knowledge graph. The knowledge graph aided us in comprehending the trends in film, television, talent, and literature. Constructing the knowledge graph presented numerous intriguing technical difficulties, including entity resolution (e.g., are these two movie titles in different languages truly identical?) and distributed graph algorithms in Spark. Following the product's introduction, analysts and scientists began unearthing previously concealed discoveries that were obscured by difficult-to-use data. The combination of overcoming technical obstacles and opening up new avenues for research was extremely satisfying.

What drew you to data engineering, Kevin?

I fell into data engineering by accident rather than making a deliberate career change. I began my career as an application developer with a working knowledge of SQL. I was later employed into my first fully data-related position, which allowed me to further develop my expertise of big data. Following that, I joined MySpace as a data engineer during its heyday and got my first experience of internet-scale data warehousing.

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What keeps me interested and enjoying data engineering is outfitting analytics engineers and data scientists with supersuits and adrenaline shots.

By simplifying something complex or by providing a safe space for my stakeholders to explore, investigate, and test, I enable them to undertake more impactful business-facing work. I like that data engineering is not the centre of attention, but rather can assist downstream analytics specialists in achieving economies of scale.

What drew you to Netflix in the first place?

My wife discovered the Netflix job posting in an effort to keep us close to her twin sister's family in Los Angeles. As a large data engineer, I discovered a plenty of potential in the Bay Area, but fewer chances in Los Angeles, where we were based at the time. Thus, the opportunity to work at Netflix was intriguing not only because it allowed me to live closer to family, but also because it provided the kind of data scale that Bay Area corporations were known for.

To begin, the organisation intrigued me, but I had no idea about the talent, culture, or leadership's mission. For years, I had been a satisfied subscriber to Netflix's DVD-rental service (no late fees!).

Following the interview, it became evident to me that this workplace culture was unlike any other I had encountered.

I was particularly taken by the trust they placed in each individual. Speaking with coworkers gave me insight into the types of people Netflix hires. The interview panel's humility, curiosity, and business acumen pushed me to join.

I was also intrigued by the potential of performing analytics on films and television shows, which was an interest of mine outside of work. It felt fortunate that the analytics field in which I'd be working aligned so well with my hobbies and interests!