Data Analytics Engineer

Role overview

A foundational Senior Analytics Engineer is needed to build out a first cloud data warehouse from the ground up. The role owns the technical infrastructure that makes reliable business measurement possible — the warehouse, ELT pipelines, and transformation layer — while partnering with stakeholders across the business on what to measure and why. The semantic layer created here will also power internal AI-assisted reporting use cases, making this a high-visibility greenfield build.

Responsibilities

- Architect and maintain the organization's cloud data warehouse as a durable foundation for analytics. - Own transformations from raw ingestion through documented, stable tables that downstream teams and tools depend on. - Design and maintain ELT pipelines that bring data from GTM, Finance, and Product tools into the warehouse. - Build the technical backbone of the BI environment — semantic layer, metric definitions, and access model — that powers executive reporting and AI-driven internal use cases. - Own data quality, governance, and lineage, including freshness checks, testing, access controls, and a documented source of truth. - Develop and maintain curated models for product usage, customer adoption, account health, and go-to-market reporting.

Requirements

- 6+ years in analytics engineering, data engineering, or a comparable role with real ownership of production data models. - Strong SQL skills and a track record shipping analytical models that other teams rely on. - Hands-on experience with a modern cloud data warehouse (Snowflake preferred; BigQuery or comparable platforms acceptable). - Extensive experience with a transformation framework such as dbt or SQLMesh. - Scripting ability in Python or similar for ingestion, validation, and automation. - Experience with a BI or semantic-layer tool such as Omni, Looker, or Tableau. - Sound judgment around data access, governance, and the handling of sensitive information.

Nice to have

- Experience building the technical foundation for AI-assisted or natural-language reporting — semantic layers designed to support LLM- or agent-based tools. - Familiarity with GTM and Customer Success data sources such as Salesforce, Gainsight, or HubSpot. - Experience applying machine learning or statistical forecasting (e.g., time-series or churn-propensity models) to predict metrics like ARR and customer retention.

Benefits and work setup

- Estimated base compensation of $160,000–$190,000 plus bonus, with the actual figure varying based on market and individual qualifications assessed during the interview process. - Comprehensive benefits package that includes 401(k) and medical/dental coverage, in addition to cash compensation. - On-site or hybrid work setup is implied, with extended periods of seated computer work expected.