Senior Analytics Engineer

Join our team as a Senior Analytics Engineer, where you will be responsible for building the modeling layer that drives our business. You will design and build trustworthy models that finance, compliance, and our product depend on. This role requires 7+ years of experience in analytics engineering, data engineering, or analytics roles with substantial modeling ownership. You will report to the VP of Data and work closely with various stakeholders to define requirements and data definitions.

• Own the transformation layer, including dimensional models, tests, and documentation, as we stand up dbt on a new warehouse.
• Turn business questions into durable models, transforming ambiguity into structure and setting patterns for the team to build against.
• Partner with finance, compliance, and product teams to define requirements, data definitions, and what “correct” means in our business context.

• 7+ years of combined experience across analytics engineering, data engineering, or analytics roles with substantial modeling ownership
• Transformation framework experience - dbt preferred, with Dataform, SQLMesh, or equivalent acceptable
• You don’t need depth in all of these, but if the work sounds like yours, please apply
• Stakeholder range - you can sit with a finance or compliance lead, ask questions to get to requirements, and leave with a definition you can build against
• Orchestration engine familiarity - Airflow, Dagster, Prefect, or equivalent
• Data modeling / dimensional modeling fluency - you can design schemas from fuzzy requirements, not just query what exists
• Strong analytical SQL skills - complex joins, window functions, performance-aware queries
• Cloud data warehouse experience - Redshift, Snowflake, BigQuery, Databricks, or equivalent
• BI / Visualization layer familiarity - Metabase, Hex, Looker, Tableau, or similar
• Engineering hygiene - Git, CI/CD, testing, documentation
• Platform-level experience - working with tools like Terraform / infra-as-code, DMS / CDC, data lake patterns, and data observability
• Experience with financial, payments, or otherwise regulated data - it shortens the learning ramp