Senior Analytics Engineer

Who you are

• 7+ years of combined experience across analytics engineering, data engineering, or analytics roles with substantial modeling ownership
• Strong analytical SQL skills - complex joins, window functions, performance-aware queries
• Data modeling / dimensional modeling fluency - you can design schemas from fuzzy requirements, not just query what exists
• Transformation framework experience - dbt preferred, with Dataform, SQLMesh, or equivalent acceptable
• Cloud data warehouse experience - Redshift, Snowflake, BigQuery, Databricks, or equivalent
• Orchestration engine familiarity - Airflow, Dagster, Prefect, or equivalent
• BI / Visualization layer familiarity - Metabase, Hex, Looker, Tableau, or similar
• Engineering hygiene - Git, CI/CD, testing, documentation
• 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
• 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
• You don’t need depth in all of these, but if the work sounds like yours, please apply

What the job involves

• Reporting To: Adam Shepard
• We’re hiring a Senior Analytics Engineer to build the modeling layer that the rest of the business runs on. This role will be responsible for the transformation layer as we stand up dbt on a new warehouse
• You will design and build trustworthy models that finance, compliance, and our product depend on. You will help architect a scalable foundation and set patterns for the team as we drive advanced analytics and reporting across the organization. You will report to the VP, Data
• 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. Not all work arrives fully specified, so you’ll turn ambiguity into structure and set patterns the team builds against
• Partner with finance, compliance, and product to define requirements, data definitions, and what “correct” means in our business context
• Monitor what you build, catching and resolving or escalating issues before our stakeholders notice
• Go a layer down into data ingestion, orchestration, or warehouse configuration when warranted, or go a layer up into analysis and decision support when needed