Senior Software Engineer, Data Engineering

About the Team & Role

The Data Engineering team builds and maintains the foundational datasets that power decision-making across Robinhood. We design reliable, scalable data systems that support product analytics, growth strategy, financial reporting, experimentation, and machine learning. The team partners closely with Product, Engineering, Data Science, and Finance to ensure accurate, well-modeled data is available across the company.

As a Senior Data Engineer, you will design, build, and evolve core datasets tracking product performance and company-wide metrics. You will develop scalable data pipelines that ingest application events and database snapshots into our data lake, ensuring high data quality and reliability. You will collaborate with application engineers to improve data generation patterns and with analytics teams to design intuitive data models.

This role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 days per week.

What You'll Do

• Help define and build key datasets across all Robinhood product areas, leading their evolution as use cases grow.
• Build scalable data pipelines using Python, Spark, and Airflow to move data from different applications into our data lake.
• Partner with upstream engineering teams to enhance data generation patterns.
• Partner with data consumers across Robinhood to understand consumption patterns and design intuitive data models.
• Ideate and contribute to shared data engineering tooling and standards.
• Define and promote data engineering best practices across the company.

What You Bring

• 5+ years of professional experience building end-to-end data pipelines.
• Hands-on software engineering experience writing production-level code in Python for user-facing applications, services, or systems (not just data scripting or automation).
• Expert experience building and maintaining large-scale data pipelines using open source frameworks (Spark, Flink, etc.).
• Strong SQL skills (Presto, Spark SQL, etc.).
• Experience solving problems across the data stack (Data Infrastructure, Analytics, and Visualization platforms).
• Expert collaborator capable of democratizing data through actionable insights and solutions.