Analytical Engineer

The role

This role sits within the Data Science and Analytics group, bridging data engineering, analytics, and data science. The engineer owns the modeled data foundation for one business area, turning source data into dependable datasets that stakeholders rely on for decisions. Work also extends into the semantic layer and AI-assisted querying tools.

What you would do

• Own the Gold-tier dimensional models for a domain, refining them continuously so dashboards and natural-language query spaces stay dependable.
• Encode the official calculation logic for core domain KPIs in versioned metric datasets that settle disagreements over figures.
• Build the semantic layer and natural-language query spaces, writing metadata, documentation, and metric definitions for plain-language access.
• Maintain the domain's metric dictionary covering each metric's meaning, owner, and location.
• Coordinate with peer analytics teams to keep overlapping KPI definitions aligned.
• Define data contracts and service levels at the handoff from refined to Gold tables, and take responsibility for quality, freshness, and on-call response for Gold failures.
• Create certified executive-grade dashboards on governed data alongside data scientists, favoring reusable products over one-off builds.
• Work with business, science, and engineering partners to shape vague questions into reusable datasets and anticipate downstream reporting impacts.

What they asked for

• Four or more years in analytics or data engineering, with a record of independently owning models a team relies on.
• Expert-level SQL with solid grounding in dimensional design, star and snowflake layouts, historical dimension tracking, and semantic modeling.
• Working knowledge of a transformation framework such as dbt, plus orchestration, version control, and lakehouse-style warehouses.
• Applied experience with data quality checks, observability, schema governance, and tuning query and model cost and performance.
• Deep familiarity with at least one business domain such as product-led funnels, sales pipeline, attribution, product telemetry, or recurring revenue.
• Ability to diagnose distrust in a figure and translate it into a concrete, lasting model change.
• Strong collaboration skills: gathering requirements, setting priorities, documenting work, driving agreement on metric meaning, and explaining tradeoffs to non-technical colleagues.
• Interest in AI-driven analytics such as text-to-SQL and semantic layers, using AI tools to extend reach while keeping analytical rigor.

Nice to have

• Experience with Unity Catalog, Looker, or LookML.
• Familiarity with reverse ETL or activation into CRM and marketing platforms.