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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 $400 million in funding from top-tier investors including Portage Ventures, Spark Capital, and Y Combinator, with a globally distributed team of 400+ members.
You will own and execute the vision for Alpaca's data transformation layer, sitting at the heart of a data platform that processes hundreds of millions of events daily from transactional databases, API logs, CRMs, payment systems, and marketing platforms. Working within a 100% remote team, you will collaborate closely with Data Engineers (managing data ingestion) and Data Scientists and Business Users (consuming your data models) to build robust, scalable data models using dbt and Trino on GCP-based, open-source data infrastructure.
Key responsibilities include:
- Designing, building, and maintaining scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics
- Establishing and enforcing best practices for data modelling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability
- Collaborating directly with finance, operations, customer success, and marketing teams to understand requirements and deliver reliable data products
- Creating repeatable patterns for integrating data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business
- Championing high standards for development, including robust change management, source control, code reviews, and data monitoring
Requirements:
- 4+ years of experience in analytics engineering or data engineering with a strong focus on the transformation (T) in ELT
- Proven track record of owning data products end-to-end, applying analytics and data engineering best practices to ensure data quality, scalability, and robust data models
- Comfortable working with ambiguity and collaborating with stakeholders to define requirements; able to take ownership with minimal oversight in a fast-paced environment
- Experience proactively identifying and implementing improvements to data warehouse performance and ETL efficiency
- Expert-level SQL and dbt skills for complex queries and data transformations
- Proficiency in Python for transformations that extend beyond SQL
- Hands-on experience with query optimization across OLTP and OLAP systems (e.g., Postgres, Iceberg)
- Proficiency with Semantic Layer modelling (e.g., Cube, dbt Semantic Layer)
- Experience owning CI/CD workflows and establishing team-wide standards for version control and code review (e.g., Git)
- Familiarity with cloud environments (GCP or AWS)
Nice to haves: Experience with data ingestion tools (e.g., Airbyte) and orchestration tools (e.g., Airflow); domain experience in brokerage operations or passion for financial markets and modelling financial datasets.
