Senior Analytics Engineer, Alpaca

• 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)

Desirable

• We are seeking an Analytics Engineer to own and execute the vision for our data transformation layer.
• You will be at the heart of our data platform, which processes hundreds of millions of events daily from a wide array of sources, including transactional databases, API logs, CRMs, payment systems, and marketing platforms
• You will join our 100% remote team and work closely with Data Engineers (who manage data ingestion) and Data Scientists and Business Users (who consume your data models).
• Your primary responsibility will be to use dbt and Trino on our GCP-based, open-source data infrastructure to build robust, scalable data models.
• These models are critical for stakeholders across the company—from finance and operations to the executive team—and are delivered via BI tools, reports, and reverse ETL systems
• 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