Senior Analytics Engineer

Join Alpaca as a Senior Analytics Engineer to design and maintain scalable data models for financial services. Work remotely and collaborate with diverse teams to enhance data quality and support business needs. Alpaca is a US-headquartered, global leader in agent-first brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. 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.

• •Own the Transformation Layer: Design, build, and maintain scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics.
• •Set Technical Standards: Establish and enforce best practices for data modeling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability.
• •Enable Stakeholders: Collaborate directly with finance, operations, customer success, and marketing teams to understand their requirements and deliver reliable data products.
• •Integrate and Deliver: Create repeatable patterns for integrating our data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business.
• •Ensure Quality: Champion high standards for development, including robust change management, source control, code reviews, and data monitoring as our products and data evolve.

• •Competitive Salary & Stock Options
• •Health Benefits
• •New Hire Home-Office Setup: One-time USD $500
• •Monthly Stipend: USD $150 per month via a Brex Card.

• •4+ years of experience in analytics engineering or data engineering with a strong focus on the "T" (transformation) 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).