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Join Alpaca as a Data Analyst to drive data-driven decision-making. Collaborate with cross-functional teams to build data models and dashboards, and explore data to provide insights into brokerage operations.
Alpaca is a US-headquartered self-clearing broker-dealer and brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. Our recent Series D funding round brought our total investment to over $320 million, fueling our ambitious vision.
Amongst our subsidiaries, Alpaca is a licensed financial services company, serving hundreds of financial institutions across 40 countries with our institutional-grade APIs. This includes broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, totaling over 9 million brokerage accounts.
Our global team is a diverse group of experienced engineers, traders, and brokerage professionals who are working to achieve our mission of opening financial services to everyone on the planet. We're deeply committed to open-source contributions and fostering a vibrant community, continuously enhancing our award-winning, developer-friendly API and the robust infrastructure behind it.
What You'll Do
- Translate questions into insight. Partner and embed with cross-functional teams to turn ambiguous business and operational questions into clear, useful analytical outputs.
- Build and own analytics data models. Write clean, maintainable SQL against PostgreSQL, Trino, and Apache Iceberg to power core financial metrics, dashboards, and analytical workflows.
- Build and iterate on metrics and tables using dbt, surfacing them through dashboards in Metabase.
- Explore data deeply to answer open-ended questions using SQL and Python in a notebook environment like Jupyter.
- Help operationalize data by identifying structural opportunities for standardizing core business entities.
- Assist with reporting capabilities and streamline report automation.
What We're Looking For
- Strong SQL fundamentals. Comfortable with joins, CTEs, window functions, and clear query structure across diverse database environments.
- Analytics Engineering Familiarity. Prior exposure to building analytical data models or metrics tables using dbt or similar analytics-engineering workflows.
- Deep Analytical Intuition. Able to ask the right questions, explore data thoughtfully, and synthesize clear, well-reasoned conclusions.
- Python & Notebook Proficiency. Comfortable using SQL as the primary analysis tool, with the ability to use Python (Pandas, NumPy) and Jupyter Notebooks.
- Clear Communicator & Collaborator. Can share insights effectively with technical and non-technical partners.
- Domain Agility. A strong desire to quickly learn the intricacies of a broker-dealer environment.
How We Take Care of You
- Competitive Salary & Stock Options
- Health Benefits
- New Hire Home-Office Setup: One-time USD $500
- Monthly Stipend: USD $150 per month via a Brex Card