Data Engineering Manager

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What the market looks like

We've tracked over 1,000 management-level data engineering postings in the US over the last six months, with concentration in California, Texas, and New York. Compensation for this cohort typically ranges from $190k to $330k annually. The strongest candidates in this space bring hands-on experience building and scaling data pipelines, proven ability to mentor engineering teams, and fluency in modern data stack technologies—cloud platforms, orchestration tools, and data warehousing solutions. Organizations are increasingly hiring for this role to support AI/ML initiatives, requiring managers who understand both engineering discipline and the unique demands of data-intensive AI systems.

Job responsibilities

• Lead and grow a team of data engineers, setting technical direction, conducting code reviews, and fostering a culture of reliability and continuous improvement

• Own the design and delivery of data pipelines and infrastructure that support analytics, AI/ML, and core business operations

• Partner with data science, analytics, and product teams to translate business requirements into scalable data architecture

• Drive adoption of best practices around data quality, testing, documentation, and deployment automation

• Manage technical roadmap and prioritization—balancing new feature delivery, platform maintenance, and technical debt

• Build and maintain relationships with cross-functional leaders; communicate trade-offs and progress to stakeholders

• Evaluate and integrate new tools and technologies into the data stack as organizational needs evolve

Candidate requirements

• 5+ years of data engineering experience, including 2+ years in a team lead or management role

• Hands-on fluency with modern data platforms—cloud data warehouses (Snowflake, BigQuery, Redshift), workflow orchestration (Airflow, dbt, Dagster), and data pipeline design

• Demonstrated ability to hire, mentor, and develop engineering talent; comfort giving and receiving feedback

• Track record of shipping robust, maintainable data systems at scale; understanding of data quality, monitoring, and incident response

• Strong communication skills—able to explain technical concepts to non-technical stakeholders and align teams around shared goals

• Experience working in fast-moving environments and adapting to evolving business priorities