Summary
What you’ll impact
The Senior Data Engineer is responsible for designing, building, and maintaining core datasets and scalable data pipelines that support product performance analytics and organization-wide metrics. This role uses Python, Spark, and Airflow on a cloud data lake, employing Presto/Spark SQL for high-quality, reliable data. Key responsibilities include ingesting application events and database snapshots, developing intuitive data models, and establishing data engineering tooling and best practices. The engineer collaborates with upstream application teams and data consumers across product, engineering, and finance to improve data generation patterns and promote standardized data solutions.
Responsibilities
What you'll do
- As a Senior Data Engineer, you will design, build, and evolve core datasets that track 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’ll collaborate with application engineers to improve data generation patterns and with analytics teams to design intuitive, well-documented data models.
- Help define and build key datasets across all Robinhood product areas. Lead the evolution of these datasets 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.
Requirements
What you’ll bring
- 5+ years of professional experience building end-to-end data pipelines
- Hands-on software engineering experience, with the ability to write production-level code in Python for user-facing applications, services, or systems (not just data scripting or automation).
- Expert at building and maintaining large-scale data pipelines using open source frameworks (Spark, Flink, etc)
- Strong SQL (Presto, Spark SQL, etc) skills.
- Experience solving problems across the data stack (Data Infrastructure, Analytics and Visualization platforms)
- Expert collaborator with the ability to democratize data through actionable insights and solutions.