Staff Data Engineer (CRASH)

Who You Are:

You're a hands-on senior data engineer who sets technical direction. You'll set the standards our data engineers build to, mentor them, and own how crash and roadway data gets from client systems into CRASH: reliably, accurately, and fast enough to keep up with new clients. You work AI-first: you use coding agents to build and test pipelines, you put AI to work on the repetitive cleaning, checking and pipeline work that slows onboarding today, and you stay accountable for the data that ships. You're a self-starter who works well with research, data science, GIS, software engineering and customer teams, and you're in the office multiple times a week.

Expected Duties:

• Own technical standards for data engineering: review designs and code, mentor the team's data engineers, and help hire.
• Lead data projects hands-on, working within the product team that owns each one: make the technical calls and see the work through to release.
• Set how the team works with AI agents, and measure whether it improves onboarding speed and data quality.
• Design, build and run data pipelines that are reliable, observable and recoverable, with alerts when a run fails or doesn't happen.
• Make client onboarding repeatable with standard ingestion and cleaning, and pipeline code built from the research team's mapping specs, so several onboardings can run in parallel.
• Apply AI to data work, such as assisted schema mapping, cleaning and anomaly detection, with checks that verify its output.
• Make data repositories agent-ready (clear context files, reliable build and test commands, checks agents can run) and build reusable skills and agents the team can share.
• Define data quality with product, data science and customer success, and build automated checks that measure it.
• Own data models and multi-tenant schemas, and bring client schemas onto a consistent standard.
• Work with GIS on geospatial data, including matching crash records to roadway networks.
• Work with platform engineering on the data platform: warehouse, orchestration, access and cost.
• Assess our data tooling and architecture (warehouse, orchestration, transformation) and lead changes where they pay off.
• Set data governance with engineering leadership and our SOC 2 program: access, retention, lineage, and handling of sensitive and government data.
• Partner with data science on datasets for analytics and models.

Required Skills & Experience:

• Bachelor's degree in Computer Science, Data Science, or a related field
• 7+ years of data engineering, including 2+ years as a technical lead (setting direction, reviewing designs, mentoring engineers)
• Strong SQL and data modeling, warehouse and relational (PostgreSQL)
• Strong Python for pipelines and tools
• Building and operating production pipelines with orchestration and monitoring (Airflow, Dagster, Prefect or similar)
• Cloud data platforms (Snowflake, BigQuery, Redshift, Databricks or similar) on AWS or another cloud
• Geospatial data (PostGIS, GeoPandas, GIS tools or similar)
• Data quality testing and validation (dbt tests, Great Expectations or similar)
• Hands-on agentic coding (Claude Code or similar): breaking work into agent-sized tasks, writing specs and tests agents can work against, and reviewing agent output critically
• Clear written communication; you document what you build

Preferred Skills:

• Transportation, crash, roadway or other public-sector data
• Multi-tenant SaaS data architecture
• Transformation frameworks (dbt or similar) and distributed processing (Spark or similar)
• Government data requirements (CJIS, state data agreements) or SOC 2
• Applying LLMs to data mapping, cleaning or extraction, with evaluation of accuracy
• Building agent tooling (MCP servers, skills or similar) that other engineers use

Salary Range: $150,000 - 200,000