Senior Analytics Engineer

Pilot.com•San Francisco, CA

1d•Remote

About The Position

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. 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. This is a strong fit for someone who strives to enable and empower others, brings a critical eye to processes and looks for opportunities to automate and streamline them, has clear opinions about what good data foundations look like, and treats AI-coding tools as a default part of how they build. Pilot's data stack is Snowflake, dbt, and Looker, with Fivetran and Airflow for ingestion and Fivetran Activations (formerly Census) for reverse-ETL. We use Claude Code or Cursor with governed MCP servers (Looker, dbt, Snowflake) as part of our day-to-day workflow.

Requirements

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)

Nice To Haves

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

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)