Staff Data Engineer

Consumer Edge is hiring a Staff Data Engineer to set technical direction for its audience data products and build and operate the pipelines that support them. The role works with data science, product, and engineering teams to create audience pipelines, onboard partner data, deliver data to partners, and guide AI-enabled engineering practices.

Responsibilities:

• Build audience pipelines. Create, size, refresh, and deduplicate audiences, with clear contracts on what each one contains
• Onboard partner data. Build repeatable patterns for bringing in partner data
• Keep partner data mappings current. Maintain identifier mappings with partners as they change, and track match quality over time
• Deliver to partners. Deliver data to external partners through APIs, files, and data shares, with tracking, retries, and alerting
• Set technical direction. Lead design reviews and build-versus-buy decisions, and mentor data engineers through design and code reviews
• Lead how the team builds with AI. Work AI-first every day, and build the agent harnesses, shared skills, and guardrails that make AI-generated work safe to ship

Requirements:

• 8+ years in data engineering or backend data systems, including production pipelines on high-volume data (consumer data is a plus)
• Ad tech or martech experience with identity and audience data
• Experience delivering data to external partners on a schedule
• Expert SQL and Python, and deep experience with BigQuery or Snowflake
• Production experience with dbt and Airflow (or a similar orchestrator), CI/CD, and infrastructure as code
• A track record of setting technical direction across teams, and clear communication with engineers and business leaders
• Comfort using AI coding tools such as Claude Code, Cursor, or GitHub Copilot as an everyday part of your work
• Integrations with ad tech platforms
• Data clean room experience
• Building AI developer tooling: agent harnesses, MCP servers, evals, or CI guardrails
• Background in ad tech, martech, or consumer data products
• Experience handling personal data under privacy rules: hashing, consent, opt-outs, and deletion