Senior Analytics Engineer, Pilot

• 5+ years experience as an Analytics Engineer or Data Engineer with end-to-end ownership of a production warehouse and modeling layer
• A track record of architecting data platforms that other teams build on
• Strong SQL and production dbt experience at meaningful scale, including layered architecture, tests, documentation, and CI
• Snowflake or comparable cloud data warehouse experience
• Experience with a semantic layer (LookML, dbt Semantic Layer, Cube, MetricFlow, or comparable)
• Experience streamlining data processes with AI dev tools, and building AI workflows or agents that teammates use day-to-day
• Working Python proficiency for ingestion code (extending or writing Airflow DAGs, building custom connectors), plus hands-on experience with managed ingestion (Fivetran or comparable) and reverse-ETL (Census or comparable)
• Cares about driving a company-wide data-driven culture through easy data access and self-service
• Drives to find new tools and ways of working, and spreads them to uplevel the team
• Takes pride in end-to-end ownership. Cares about how the platform looks and works, with a point of view about where it should go

Desirable

• As the Senior Analytics Engineer on this team, you'll own that foundation end-to-end.
• You'll architect the warehouse and the semantic layer, bring new data sources online as the business expands, and operate it like a well-oiled production system.
• The work you ship is the foundation every team at Pilot reports, decides, and forecasts against. You own how Pilot's data is shaped, modeled, and surfaced for AI tools, so every team can integrate AI into their workflows safely and reliably
• Architect Pilot's data foundation: the warehouse layout, the semantic layer, and the access patterns that let humans and AI agents use Pilot's data safely and well
• Methodically plan what each domain's data needs to look like, then ship it as durable, well-documented, well-tested models in dbt
• Lead the ingestion of new data sources as the business expands: scope what's needed, choose the right pattern (Fivetran, custom Airflow DAG, or partner share), and ship it production-grade with tests and docs
• Keep the foundation reliable and clean: operate it as a production system and continuously retire what's stale
• Build AI tools and workflows that uplevel the data team's own work (Claude Code skills, MCP-driven agents)
• Enable other teams to safely AI-enable their own workflows on Pilot data. For example, scope governed access surfaces, build the patterns that route them to the semantic layer first, and partner on intake review for new Claude Project integrations
• Set the technical standards: testing, documentation, naming, materialization, deprecation, code review
• Own Pilot's canonical metrics across the company (including Finance, Sales, Operations, and Marketing)