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Alpaca is a US-headquartered global leader in agent-first brokerage infrastructure, serving hundreds of financial institutions across 40 countries with institutional-grade APIs. The company is backed by $400M in funding from top-tier investors including Portage Ventures, Spark Capital, and Y Combinator, with a team of 400+ globally distributed members.
You will own and execute the vision for Alpaca's data transformation layer, sitting at the heart of their data platform that processes hundreds of millions of events daily from transactional databases, API logs, CRMs, payment systems, and marketing platforms. Working on a 100% remote team, you'll collaborate closely with Data Engineers (who manage data ingestion) and Data Scientists/Business Users (who consume your models).
Key responsibilities include designing and building scalable data models using dbt and SQL on GCP-based, open-source infrastructure to support diverse business needs—from monthly financial reporting to near-real-time operational metrics. You'll establish and enforce best practices for data modeling, development, testing, and monitoring to ensure data quality and integrity at cent-level precision. You'll enable stakeholders across finance, operations, customer success, and marketing by understanding their requirements and delivering reliable data products. You'll create repeatable patterns for integrating data models with BI tools and reverse ETL processes, and champion high standards for development including robust change management, source control, code reviews, and data monitoring.
Required: 4+ years in analytics engineering or data engineering with strong focus on transformation (ELT). You must have a proven track record owning data products end-to-end, applying best practices for data quality and scalability. You're comfortable with ambiguity, can collaborate with stakeholders to define requirements, and take ownership with minimal oversight in fast-paced environments. Expert-level SQL and dbt skills, proficiency in Python for transformations beyond SQL, hands-on experience with query optimization (Postgres, Iceberg), semantic layer modeling (Cube, dbt Semantic Layer), CI/CD workflows and version control (Git), and familiarity with cloud environments (GCP or AWS) are essential.
Nice-to-haves include experience with data ingestion tools (Airbyte), orchestration tools (Airflow), and domain experience in brokerage operations or passion for financial markets.
