Data Engineering Lead
$190,000–$220,000 year
HybridChicago, Illinois, United States
Job Summary
Establish and enforce architecture standards for consistent data designs while assessing current infrastructure to guide refactoring and replacement decisions. Lead the build-out of modern ELT pipelines, stream ingestion, and transformation logic, serving as the technical authority on storage, compute, and model design. Directly manage the data science team by setting priorities, translating business problems into scoped projects, and fostering technical rigor through mentorship and code review. Own pipeline monitoring and observability, ensuring alerting, data quality checks, and incident response processes catch failures early. Define and maintain the data model RFC process to enforce boundary discipline and prevent undocumented changes. Provide regular visibility into team progress, architectural decisions, and risks to senior leadership, escalating business commitment risks when necessary.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
- 6–10 years of experience in data engineering or a closely related discipline
- 2+ years in a technical lead or staff-level individual contributor role
- Familiarity with data science workflows, ML model lifecycle, and MLOps concepts
- Expertise in data engineering — including model design, testing strategy, incremental patterns, and layer boundary governance
- Experience designing and enforcing data modeling standards in a team environment — not just building models, but establishing the patterns others follow
- Demonstrated ability to bring a team along technically — through code review, documentation, mentorship, and setting standards that stick
- Familiarity with CI/CD for data pipelines (GitLab or equivalent) — including automated testing, deployment workflows, and environment promotion strategies
- Experience with data observability and monitoring — alerting on pipeline failures, data quality degradation, and freshness SLAs (familiarity with tools like Elementary, Monte Carlo, or equivalent)
- Strong decision-making under ambiguity — this role requires someone who can move forward with incomplete information and course-correct, not someone who needs consensus to proceed
- Clear, direct communicator who can translate technical tradeoffs for non-technical stakeholders
- Technical expertise in: Python, Apache Airflow, Apache Kafka, cloud data warehouses (Redshift, GBQ, Databricks)
- Hybrid position that requires in office presence 3 days a week (Tuesday-Thursday) in Chicago
Desired Qualifications
- Advanced degree in a technical field
- Experience at a company that has completed a similar legacy-to-modern warehouse migration
- Prior experience managing or mentoring data engineers
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