Data Analyst

Partner with cross-functional teams to translate business questions into analytical outputs. Build and maintain data models in SQL and dbt, create dashboards in Metabase, and use Python for deeper analysis.

About the job

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. Quickly assimilate subject matter expertise across brokerage operations, ledger systems, and compliance.
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 to help teams understand transaction volumes, revenue streams, and system performance over time.
Explore data deeply to answer open-ended questions. Use SQL as your primary tool, with Python in a notebook environment like Jupyter when helpful, to investigate trends, anomalies, complex profit and loss (P&L) calculations, and trading behaviors, connecting analyses back to real business questions.
Help operationalize data. Identify structural opportunities for standardizing core business entities, proactively transitioning ad-hoc analytical requests into intuitive, user-friendly self-serve environments.
Assist with reporting capabilities and streamline report automation, including servicing requests in a timely manner, cataloguing recurring reports, and driving conversations around standardization and productization of data reports.

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 without getting lost in rabbit holes.
Python & Notebook Proficiency. Comfortable using SQL as the primary analysis tool, with the ability to use Python (Pandas, NumPy) and Jupyter Notebooks for deeper, more efficient programmatic analysis.
Clear Communicator & Collaborator. Can share insights effectively with technical and non-technical partners, acting as a bridge between data infrastructure and business operations.
Domain Agility. A strong desire to quickly learn the intricacies of a broker-dealer environment, including clearing infrastructure, execution lifecycles, and financial accounting standards.