Lead Data Engineer

What you'll do

• Own data product design, dimensional models, ODCS contracts, and compatibility decisions for changes affecting consumers.
• Set engineering, quality, observability, operational, and regulated-carrier controls; oversee incidents, pipelines, and delivery practices.
• Lead delivery planning, code review, mentoring, and collaboration with platform, ML, product, architecture, and governance teams.

What they ask for

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; equivalent practical experience considered.
• 6+ years building and operating production data pipelines and consumer-facing data models.
• Strong hands-on Python and SQL; hands-on experience with Databricks or comparable Spark-based lakehouse technology, including Delta Lake, MERGE, incremental processing, and performance tuning.
• Deep dimensional modeling experience, technical leadership, ownership of data relied on by other teams, and experience with orchestration, Git-based development, code review, CI/CD, and data observability/SLA/SLOs.

Nice to have

• Databricks certification; Unity Catalog, dbt, Dagster, data contracts/ODCS, or data-quality and observability tooling experience.
• MLOps familiarity, Azure and Azure DevOps, regulated-industry experience, or disciplined introduction of AI-assisted development.

Pay

USD 109,500–167,833 per-year-salary (base salary)

Tools

Python, SQL, Databricks, Delta Lake, dbt, Azure DevOps, Dagster, Airflow

AI focus

Partner with ML engineering to provide contracted, versioned, reproducible Gold-layer training or feature data; familiarity with MLOps is preferred. Experience introducing AI-assisted development is also preferred.