Data Analyst

Alpaca · Posted 4w ago

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Eligibility

Americas

Type

Full-time

Posted

4w ago

Tech

PythondbtSQLPostgreSQLTrinoNumPy

Who We Are:

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, totalling 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.

Alpaca is proudly backed by top-tier global investors, including Portage Ventures, Spark Capital, Tribe Capital, Social Leverage, Horizons Ventures, Unbound, SBI Group, Derayah Financial, Elefund, and Y Combinator.

Our Team Members:

We're a dynamic team of 380+ globally distributed members who thrive working from our favorite places around the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, the UK, and beyond!

We're searching for passionate individuals eager to contribute to Alpaca's rapid growth. If you align with our core values—Stay Curious, Have Empathy, and Be Accountable—and are ready to make a significant impact, we encourage you to apply.

Your Role:

We're looking for a Data Analyst to help make Alpaca data-driven from the ground up. You will join a highly collaborative team, partnering closely with cross-functional departments (Finance, Operations, Compliance) to build foundational data models, dashboards, and deep-dive analyses that our teams rely on every day.

This role is ideal for someone who wants to take on meaningful ownership, embed deeply with subject matter experts to quickly assimilate complex brokerage knowledge, and dig deeply into data to understand why things are happening, not just what is happening.

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. You will 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. 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.