Data Engineering Manager

Who You Are:

You're a data engineering leader who still builds. You'll lead Citian's data engineers and own how crash, roadway and asset data gets from client systems into CRASH and ADAPT: 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 mapping, cleaning and checking that slows onboarding today, and you stay accountable for the data that ships. You're a self-starter who works well with data science, GIS, software engineering and customer teams, and you're in the office multiple times a week.

Expected Duties:

• Lead and grow the data engineering team: set priorities, review work, mentor, and hire.
• 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, cleaning and mapping, 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 leading 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