• Set the technical direction for the data engineering team • Own the strategy, maintenance, and operations of our data platform • Lead a team of data engineers while staying hands-on with architecture decisions and technical leadership • Maintain and build feature stores and ML infrastructure to power our machine learning models

• Provide guidance and mentorship to managers and individual contributors on the team. • Develop strong relationships with data science, engineering, and business partners, and improve how we leverage data at Netflix. • Help nurture the data engineering community at Netflix, which extends beyond your immediate team and requires working with other senior leaders • Expand your team by attracting diverse talent that can push our technical boundaries. • Foster an inclusive environment, where all points of view are welcomed and encouraged. • Be a thoughtful leader, and drive both technical and business priorities and roadmaps.

• Design, build, and own ETL pipelines that extract, transform, validate, and load healthcare data from external sources into our platform using Python and SQL. • Implement robust data quality checks and monitoring to ensure migrations are accurate, complete, and repeatable, with particular attention to edge cases and long‑tail records. • Handle a mix of one‑off data requests and long‑lived, reusable data flows, making thoughtful tradeoffs between quick scripts and durable systems. • Collaborate with product, implementation, and support teams to diagnose and resolve customer data issues, often working from real tickets and real timelines. • Contribute to internal standards, templates, and tools that make future migrations faster and more reliable across customers. • Participate in code reviews and technical discussions with peers, bringing prior experience and good judgment rather than relying on close day‑to‑day mentorship.

• Designing and maintaining scalable, secure data pipelines that feed BigQuery from diverse sources • Owning our infrastructure-as-code setup using Terraform • Automating data QA, modeling, and maintenance tasks using scripting and AI • Optimizing query performance and minimizing latency across the data stack • Guiding data governance, including implementing role-based access control and security policies