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

• You don’t need depth in all of these, but if the work sounds like yours, please apply

• 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

What the job involves

• 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