Senior Data Engineer
About the role
Pilot is hiring a Senior Analytics Engineer to own our data foundation: the warehouse, the semantic layer, and the ingestion that every team at Pilot runs on.
Pilot launched in 2017 to bring the back office into the modern era. Today, with 3,000+ customers and a fast-growing roster of AI-native services, the company runs on data, and the foundation underneath it is being rebuilt to keep up.
Responsibilities
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)
Requirements
5+ years experience as an Analytics Engineer or Data Engineer with end-to-end ownership of a production warehouse and modeling layer
Strong SQL and production dbt experience at meaningful scale, including layered architecture, tests, documentation, and CISnowflake 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)
About You
Even if you don't have experience with the specific technologies in our stack, we'd love to talk to you!
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
Nice to Have
Experience setting up safe, audited AI access to a data warehouse (allow-listed schemas, audit logs, kill switches)
Experience designing data platforms that non-data teams build self-serve workflows on top of
Airflow production experience
dbt Cloud experience
Looker administration experience (permissions, content curation, Spectacles, LAMS)
Data observability experience (Elementary, Monte Carlo, or comparable)
Fintech, accounting software, or B2B SaaS data background