Senior Analytics Engineer

We're hiring a Senior Analytics Engineer within our centralized data team to own a core area of our data, such as financial performance, operations, or growth and retention, from raw sources through to the metrics leadership uses to make decisions. You'll build the dbt models and semantic layer behind those metrics. You'll also act as the analytical partner to the teams that rely on them.

This is a high-ownership role on a lean team that moves fast. Requirements often start ambiguous, and you'll be expected to find the highest-value problems before they reach anyone's roadmap.

• →Own a functional data area. Be the lead analyst for a domain like finance or growth and retention, the first person stakeholders turn to for answers and insight. You'll own its models, metrics, analysis and roadmap.
• →Architect core data models. Design and maintain silver and gold layer models in dbt using Kimball dimensional modeling (facts and dimensions). These power segmentation, targeting, automation and analytics for your area.
• →Define and operationalize metrics. Turn company-wide metrics into governed, consistent definitions. Examples include retention, funnel conversion, LTV/CAC/ROAS and revenue.
• →Steward the semantic layer. Treat our BI tooling (Hex) as a product. Own semantic layer health, cross-team modeling standards and self-serve data products, beyond building dashboards on request.
• →Make the numbers trustworthy. Set standards for testing, documentation and freshness. Lead root-cause analysis on data discrepancies, then fix the underlying architecture so they don't recur.
• →Partner with the business. Turn ambiguous questions from Marketing, Growth, Finance and Product into clear requirements and scalable solutions. Work without dedicated PM support and use data to influence decisions.
• →Communicate clearly. Explain technical decisions, trade-offs and results to audiences from engineers to senior leadership. Set and manage expectations along the way.
• →Improve upstream data. Work with product and engineering on event taxonomy, instrumentation quality and consistency across app, web and backend.

• 5+ years in analytics, analytics engineering, data engineering or a closely related role.
• Deep SQL in a cloud data warehouse, ideally Snowflake. You use it to validate data quality, investigate anomalies and back your own analysis.
• Production experience with dbt: modeling, testing, documentation and project structure.
• Strong data modeling skills, including Kimball dimensional modeling (star schemas, slowly changing dimensions) and layered medallion architectures. You know when to break the rules.
• Business and analytical judgment. You can tell which question matters and how to answer it, not just how to model it.
• A track record of leading cross-team data initiatives, including gathering requirements yourself from technical and non-technical stakeholders.
• Clear, confident communication with senior leadership, including pushing back and resetting expectations when needed.
• Comfort with ambiguity and a fast pace. You prioritize well and ship without waiting for perfect specs.
• Hustle and a bias for action. You roll up your sleeves, do what the moment needs, whether that's a quick ad-hoc pull or a weekend fire drill on a broken pipeline, and follow through until it's done.

• Experience supporting Marketing, Growth, Sales or Product teams, such as attribution, paid media, funnels or retention.
• Experience owning a BI platform or semantic layer (Hex, Looker, dbt Semantic Layer or similar) as a product.
• Experience building semantic layers and documentation designed for AI and agentic workflows.
• Familiarity with product analytics instrumentation and event tracking.
• Background in consumer health, subscription or e-commerce businesses.