Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades—the largest transfer of wealth in human history. We're building the future of finance and invite you to be at the epicenter of this historic cultural and financial shift.

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. 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. This role is based in Toronto, ON, with in-person attendance expected at least 3 days per week. 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. 3+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field. ~ Solid 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, or Tableau. ~ Strong familiarity with version control (GitHub), CI/CD, and modern development workflows. ~ Data Engineering experience. Familiarity with HR systems like Greenhouse, Workday, and One Model.

Satisfaction from mentoring and encouraging others in your field. In addition to the base pay range listed below, this role is also eligible for bonus opportunities, equity, and benefits. Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands.

Base Pay

Range (Toronto, ON): $145,000 – $170,000 CAD #

📌 Analytics Engineer (Winnipeg)
🏢 Unchain Data
📍 Winnipeg