Data Scientist, Robinhood

• The business is seeking curious, growth-minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you’re invigorated by our mission, values, and drive to change the world — we’d love to have you apply
• 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 mindset
• Ability to work in a fast-paced, and highly cross-functional environment

Desirable

• Robinhood is a metrics driven company, and the Data Science Org is at the center of all the metrics initiatives, setting foundations to all key decisions from growth strategy to product optimization to our day-to-day operations
• We are looking for a Senior Analytics Engineer to drive end-to-end development of high-performing ETL data pipelines, tools, and dashboards to scale insight generation to meet critical business, product, finance, and regulatory requirements
• We are a fast-paced team in a fast growing company and this is a unique opportunity to help lay the foundation for reliable, impactful, data-driven decisions across the company for years to come
• Partner closely with product, engineering, and data science to scope out data, metrics & analytics initiatives
• Lead end-to-end development of high-performing ETL data pipelines, tools, and dashboard to scale insight generation to meet critical business, product, and regulatory requirements
• Collaborate with other data scientists to transform raw data into highly actionable insights, facilitating analytics deep dives to turn those insights into growth-driven actions
• Build frameworks, tools, and workflows that maximize efficiency for data consumers, while maintaining high standards of data quality and performance