FINNY Senior Analytics Engineer owns the analytics warehouse end-to-end, defining metrics, models, and reports that the company relies on. Partners with stakeholders to translate questions into data models and maintain BI tools and semantic layers.
Responsibilities
FINNY is a metrics-driven company. Until now, our analytics warehouse has been a shared role, divided across several teams. We're bringing it together in a new structure, on a new foundation, and we're hiring a senior Analytics Engineer to own it end to end: the models, the definitions of the metrics the company runs on, and the reports people trust.
You'll work directly with operations, sales, marketing, growth strategy, finance, product and engineering. Success looks like: the metrics the company runs on have tested definitions, a small set of reports is the first place people look, AI tools answer questions about those metrics correctly, and every key business area is covered to the same standard. As the data team grows, you'll help hire and lead it.
Own the analytics warehouse day to day: staging and core models, dimensions, facts and identity resolution across our systems
Define the metrics FINNY runs on, with tests and documentation, so every team works from the same numbers
Partner with internal stakeholders across the company to turn their questions into models and reports they use
Own the BI layer: choose the tool and semantic layer, and keep a small set of authoritative reports
Make the warehouse something AI tools can answer from correctly
Run the dbt project like software: version control, code review, CI, tests and documentation
Qualification
Strong SQL and dimensional modelingScrappyYou're excited to work in personFirst or early data hire at a startupModeled complex pricingMade data usable by AI agents
Required
Typically 4+ years in analytics engineering, data engineering or analytics, including owning models that other teams relied on
Strong SQL and dimensional modeling: facts, dimensions, conformed keys, and a clear grain for every table
dbt (or an equivalent tool) in production, with git, tests and CI
You've defined business metrics people used to make decisions, and fixed the cause when two numbers disagreed
You work directly with non-technical partners and turn vague questions into clear answers—and say "not yet" with a reason
Scrappy, with ownership: you jump in where help is needed, including work outside your job description
You're excited to work in person, 5 days a week, in our Chelsea, NYC office
Preferred
First or early data hire at a startup
Modeled complex pricing, billing, subscription or usage data
Made data usable by AI agents: documentation, a semantic layer, guardrails
