Data Engineering Lead

Citian • Washington, DC

10d • Hybrid

About The Position

Citian is a fast-growing, venture-backed SaaS technology company based in Washington, DC, developing software for transportation and infrastructure systems. The Data Engineering Lead will lead Citian’s data engineers while remaining hands-on, owning the flow of crash, roadway, and asset data from client systems into CRASH and ADAPT. The role focuses on making data onboarding reliable, accurate, repeatable, and scalable as the company adds clients.

The lead will establish how the team uses AI agents in data engineering, apply AI to repetitive mapping, cleaning, and checking tasks, and remain accountable for the quality of delivered data. The role works closely with data science, GIS, software engineering, platform engineering, product, customer success, and customer teams. The position is expected to work from the office multiple times a week.

Citian’s software aims to reduce traffic fatalities, improve pedestrian accessibility, and help transportation system operators save time and money. The company applies emerging technologies, AI, and machine learning to tools for clients across the United States, with global ambitions.

Requirements

• Bachelor’s degree in Computer Science, Data Science, or a related field.
• At least 7 years of data engineering experience, including at least 2 years leading engineers.
• Strong SQL and data modeling skills, including experience with data warehouses and relational databases such as PostgreSQL.
• Strong Python skills for pipelines and tools.
• Experience building and operating production pipelines using orchestration and monitoring tools such as Airflow, Dagster, Prefect, or similar.
• Experience with cloud data platforms such as Snowflake, BigQuery, Redshift, or Databricks on AWS or another cloud.
• Experience with geospatial data using PostGIS, GeoPandas, GIS tools, or similar.
• Experience with data quality testing and validation tools such as dbt tests, Great Expectations, or similar.
• Hands-on experience with agentic coding tools such as Claude Code or similar, including breaking work into agent-sized tasks, writing specifications and tests for agents, and critically reviewing agent output.
• Clear written communication and a habit of documenting work.

Nice To Haves

• Experience with transportation, crash, roadway, or other public-sector data.
• Experience with multi-tenant SaaS data architecture.
• Experience with transformation frameworks such as dbt and distributed processing such as Spark.
• Familiarity with government data requirements such as CJIS or state data agreements, or SOC 2.
• Experience applying LLMs to data mapping, cleaning, or extraction, including evaluating accuracy.
• Experience building agent tooling such as MCP servers or reusable skills for other engineers.

Responsibilities

• Lead and grow the data engineering team by setting priorities, reviewing work, mentoring engineers, and hiring.
• Establish how the team works with AI agents and measure the impact on onboarding speed and data quality.
• Design, build, and operate reliable, observable, and recoverable data pipelines, including alerts for failed or missed runs.
• Make client onboarding repeatable through standardized ingestion, cleaning, and mapping processes that support parallel onboardings.
• Apply AI to data tasks such as assisted schema mapping, cleaning, and anomaly detection, with checks to validate outputs.
• Make data repositories ready for agents with clear context files, dependable build and test commands, and executable checks; develop reusable skills and agents for the team.
• Define data quality with product, data science, and customer success, and create automated checks to measure it.
• Own data models and multi-tenant schemas, and standardize client schemas.
• Work with GIS on geospatial data, including matching crash records to roadway networks.
• Work with platform engineering on the data platform, including the warehouse, orchestration, access, and cost.
• Assess data tooling and architecture, including warehouse, orchestration, and transformation, and lead worthwhile changes.
• Establish data governance with engineering leadership and the SOC 2 program, covering access, retention, lineage, and handling sensitive and government data.
• Partner with data science to prepare datasets for analytics and models.

Benefits

• Competitive salary and benefits package.
• Medical, dental, and vision insurance.
• Generous paid time off.
• 401(k) company match.
• Monthly commuter transportation benefit.
• On-site gym.
• Free snacks in the office.
• High-growth potential and opportunities for advancement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior