This post is part of us “Netflix Data Engineers” Series, where our own data engineers talk about their journey Data Engineering: Netflix.
Kevin Wiley Content Data is a data engineer in the Science and Engineering team. In this post, Kevin talks about his extensive experience in content analysis on Netflix since joining more than 10 years ago.
Kevin grew up in the Washington, D.C. area and earned a bachelor’s degree in mathematics from Virginia Tech. Prior to joining Netflix, he worked at MySpace, helping with the implementation of page classification, path analysis, sessionization and much more. In her spare time she enjoys gardening and sports with her 4 kids.
Her favorite TV shows: Ozark, Breaking Bad, Black Mirror, Barry and Chernobyl
Since I joined Netflix in 2011, my favorite project is designing and building the first edition of our Entertainment Knowledge Graph. Knowledge Graph has enabled us to better understand the trends in movies, TV shows, talents and books. Creating knowledge graphs involves many interesting technical challenges such as entity resolution (e.g., are the names of these two movies really the same in different languages?), And Spark distributes graph algorithms. After we launched the product, analysts and scientists began to reveal new insights that were previously hidden behind the hard data used. The combination of overcoming technical barriers and creating new opportunities for analysis was fruitful.
I stumbled upon data engineering instead of deliberately leading a career into the field. I started my career as an application developer with a basic introduction to SQL. Later I was assigned to my first pure data gig where I was able to deepen my knowledge about big data. After that, I came back to MySpace as a data engineer and got my first taste of data warehouse on the internet-scale.
What keeps me busy and enjoys data engineering is giving super-suits and adrenaline shots to analytical engineers and data scientists.
When I think of something complex as simple, or create a clean environment for my stakeholders to explore, research and test, I empower them to do more effective business-oriented work. I like that data engineering is not in the limelight, but instead downstream analysis can help create scale economics for professionals.
My wife came to Los Angeles to post a job on Netflix in an effort to keep us with her twin sister’s family. As a big data engineer, I noticed that there were plenty of opportunities in the Gulf region, but opportunities were even more limited in LA where we were at the time. So the opportunity to work at Netflix was exciting because it allowed me to stay close to family, but also provided the most common type of data scale for Bay Area companies.
The company was keen to get started, but I knew nothing of talent, culture or leadership perspectives. I have been a happy customer of Netflix’s DVD-rental program (no late fees!) For years.
After the interview, it became clear to me that the culture of this company was different from my experience.
I was especially interested in putting their trust in every employee. Talking to coworkers gives me an idea of the kind of people Netflix hires. The humility, curiosity and business acumen of the interview panel was quite impressive and inspired me to join them.
I was also thrilled to see the possibility of analyzing in movies and TV shows, which I enjoyed exploring outside of work. It seemed strange that the field of analysis I would work in would match my hobbies and interests so well!
When I joined Netflix in 2011, our content analysis team was just people. We had a small office in Los Angeles focused on content, and significantly more staff at Los Gatos headquarters. The company was initially thought of as a tech company.
At the time, the data engineering team primarily used a data warehouse ETL tool called Ab Initio and an MPP (Massively Parallel Processing) database for warehousing. Both had equipment located in our own data center. Hadup was being tested lightly, but only in a few high-scale areas.
Fast Forward 10 years, and Netflix is now the leading streaming entertainment service – serving members in more than 190 countries. In the data engineering space, very little of the same technology remains. Our data centers have been replaced by Retired, Hadup Spark, and Initiative, and our MPP databases no longer fit into our larger data ecosystem.
In addition to companies and technology transfers, my role has evolved somewhat as our company has grown. When we were a small company, the ability to expand multiple functions was invaluable for agility and speed of delivery. The sooner we can receive new data and create dashboards and reports for non-technical users to explore and analyze, the sooner we can deliver results. But now, we have a much more mature business, and many more analyst stakeholders that we serve.
Over the years, I was in a management role, leading a great team with a variety of backgrounds and skill sets. However, I missed creating data products on my own so I wanted to go back to a hands-on engineering role. My boss kindly let me make this change and focused on influencing the business as a personal contributor.
I think about my future on Netflix, what inspires me is basically the same thing I’ve always been a fan of. I want to make the lives of data consumers easier and enable them to be more influential. As companies scale and we continue to invest in storytelling, the opportunity for me to influence these decisions through better access to information and insights increases for me. The biggest impact I can make as a data engineer is to create a scale economy by producing data products that will serve a variety of applications and stakeholders.
If I could create beautifully simple data products for analytics engineers, data scientists and analysts, we could all be better at Netflix’s goal: the entertainment world.
Interested in learning more about the role of data in Netflix? You are in the right place! Keep an eye out for our open role in data science and engineering by visiting our job site. Our culture is the key to our influence and growth: read about it here. To learn more about our data engineers, check out our chats with Devi Rajendran and Samuel Setagen.