Staff Analytics Engineer at Alpaca

About Alpaca

Alpaca is a US-headquartered, global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, and 24/5 trading. As a licensed financial services company, we serve hundreds of financial institutions across 40 countries, including broker-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, supporting over 10 million brokerage accounts. Our global team is a diverse group of experienced engineers, traders, and brokerage professionals dedicated to opening financial services to everyone on the planet. We are deeply committed to open-source contributions and fostering a vibrant community while continuously enhancing our award-winning, developer-friendly API and robust infrastructure. Alpaca is strongly backed by over $400 million from top-tier global investors including Portage Ventures, Spark Capital, and Y Combinator.

Our Team

We are a dynamic team of over 400 globally distributed members who thrive working from their favorite places across the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, UK, and beyond. We are 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.

The Analytics Engineering Team & Role Overview

The Analytics Engineering team builds the core warehouse that Alpaca operates on. This team transforms hundreds of millions of daily events from transactional databases, API logs, CRMs, payment systems, and marketing platforms into the critical datasets used for partner invoicing, regulatory reporting, executive KPIs, partner-facing products, and an increasing number of AI agents. As a Staff Analytics Engineer, you will own the future of our warehouse, defining its modeling approach, performance, and serving strategy. You will collaborate across Data Platform, Data Scientists, and business users, while also advising leadership. Your role extends beyond simply building models; you will define how the entire company models, tests, and serves trusted data, ultimately setting the standard for data practices across the organization.

Key Responsibilities

• Set the multi-quarter technical direction for the warehouse. This involves defining layering (staging, intermediate, marts), domain boundaries, and materialization strategies to ensure over 1,000 dbt models remain fast, testable, and cost-efficient as we scale 10x.
• Determine the appropriate serving layer for models: when they belong in ad hoc OLAP queries on the lakehouse, real-time analytics engines for low latency, or governed metrics in a semantic layer.
• Establish schemas, freshness SLAs, and change notification protocols with source owners, deciding between Change Data Capture (CDC) or batch processing based on consumer needs.
• Guarantee cent-level accuracy on financial data through rigorous testing, reconciliation, and write-audit-publish patterns to prevent silent errors from reaching users.
• Define organizational standards for modeling, testing, and Continuous Integration/Continuous Deployment (CI/CD). You will also mentor engineers and build alignment across various teams.
• Prioritize work that drives metrics for finance, operations, and partners, proactively addressing scaling, quality, and cost challenges before they escalate.

Must Have Qualifications

• 7 or more years of experience in analytics engineering or data engineering, with a focus on transformation and warehouse architecture, along with Staff-level leadership experience in setting direction and upskilling teams.
• Demonstrated leadership in leading an organization-wide warehouse migration or redesign, such as a mart restructure, semantic layer rollout, or testing and CI overhaul.
• Expert knowledge of query performance on distributed engines like Trino, Presto, or Spark. This includes analyzing query plans, partitioning, and file layout management on Apache Iceberg.
• A clear architectural view on the optimal placement of datasets within a lakehouse query engine, a real-time OLAP engine (like ClickHouse), or a semantic layer.
• Expert proficiency in SQL and dbt, complemented by Python for transformations extending beyond standard SQL capabilities.
• Proven experience defining data contracts and freshness SLAs with upstream producers, and a solid understanding of the trade offs between CDC and batch ingestion patterns.
• Thrives in ambiguity. Able to take a vague, high stakes problem from initial concept to a fully realized solution with minimal supervision.

Nice to Have Qualifications

• Production experience modeling data specifically for ClickHouse or another real-time OLAP engine.
• Familiarity with AI and agentic analytics, such as making data usable by Large Language Models (LLMs) and building evaluation frameworks.
• Experience with tooling like Airflow, Airbyte, and CDC technologies such as Debezium or Redpanda.
• Direct experience within the brokerage or financial markets domain.

Compensation and Benefits

At Alpaca, we invest in our people. We offer: Competitive Salary & Stock Options, Comprehensive Health Benefits, a New Hire Home-Office Setup valued at $500 USD, and a Monthly Stipend of $150 USD via a Brex Card.

Alpaca Commitment

Alpaca is proud to be an equal opportunity workplace dedicated to pursuing and hiring a diverse workforce. We strongly encourage applications from all backgrounds.