Analytics Engineer, Robinhood

• 3+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field
• Strong expertise in advanced SQL, Python scripting, and Apache Spark (PySpark, Spark SQL) for data processing and transformation
• Proficiency in building, maintaining, and optimizing ETL pipelines, using modern tools like Airflow or similar
• Experience in building polished and performant dashboards using tools like Superset, Looker, Tableau
• Strong familiarity with version control (GitHub), CI/CD, and modern development workflows
• A strong product approach
• Ability to work in a fast-paced, and highly cross-functional environment

Desirable

• Robinhood’s Analytics Engineering team, part of the Data Science organization, is the backbone of our decision-making ecosystem
• We design and deliver foundational data products that power everything from product innovation to regulatory compliance and operational excellence
• Our mission is simple but ambitious: enable every team at Robinhood to access trustworthy, scalable, and self-serve analytics—so the right decisions happen faster
• We operate at the intersection of data engineering, data science, and product strategy, collaborating closely with product managers, engineers, and data scientists to transform raw data into clear, actionable intelligence
• As an Analytics Engineer, you will be a key architect of Robinhood’s data foundation
• You’ll own the design and development of high-performance ETL pipelines, data models, and analytics tools that fuel critical decisions across the company
• Your work will directly influence product strategy, regulatory reporting, and operational efficiency, ensuring Robinhood remains agile and data-driven at scale
• This is more than a build role—you’ll help define metrics, shape datasets, and set the standards for analytics excellence across the company
• The systems and frameworks you create will have a long-lasting impact on Robinhood’s growth trajectory
• Partner cross-functionally with product, engineering, and data science teams to scope and deliver high-impact analytics initiatives, from metric definitions to fully automated reporting solutions
• Design and maintain reliable, scalable ETL pipelines and data models using modern data tools (e.g., Airflow, Spark), ensuring performance and accuracy at scale
• Lead end-to-end development of analytics products—from ingestion to visualization—that meet mission-critical business, product, and regulatory needs
• Build internal frameworks and tooling to make high-quality data more accessible and actionable across the organization
• Collaborate with data scientists to transform raw data into meaningful insights that directly shape business and product outcomes
• Champion analytics best practices and drive a culture of data literacy, empowering teams to confidently explore and interpret data on their own