Director, Data Engineering
United States · Remote · Permanent
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What the market looks like
We've tracked 200+ senior-level data engineering postings across the US in the last six months, with the strongest concentration in California, Texas, and New York. Hiring is spread across IT services, professional services, financial services, healthcare, and manufacturing — sectors all racing to scale data infrastructure for AI workloads. Compensation for this cohort ranges from $220K to $440K annually. The strongest candidates bring 8+ years of hands-on data engineering experience, have shipped large-scale data platforms end-to-end, and can speak fluently to both the technical architecture and the business outcomes it enables.
Job responsibilities
Own the design, build, and optimization of data pipelines and infrastructure that power analytics, AI, and machine learning initiatives across the organization
Lead a team of data engineers and analytics engineers; set technical direction, mentor IC growth, and drive execution on roadmap priorities
Partner with data science, ML engineering, and analytics teams to understand data requirements and translate them into scalable platform solutions
Drive decisions on data stack architecture — storage, compute, orchestration, data quality — and shepherd migrations or modernizations when needed
Establish data governance, quality standards, and documentation practices that scale with organizational growth
Own performance and cost optimization for data infrastructure; track and report on pipeline SLOs and system health
Collaborate with product, engineering, and business teams to prioritize data platform features and resolve blockers in real time
Candidate requirements
8+ years of data engineering experience, with at least 3 years in a senior individual contributor or team lead role shipping production data systems
Deep fluency with modern data stack tools: cloud data warehouses (Snowflake, BigQuery, Redshift), orchestration (Airflow, dbt, Prefect), and streaming or batch pipeline frameworks
Proven ability to design and scale data platforms from architecture through implementation; experience optimizing for cost, latency, and reliability
Strong leadership presence: you've hired, mentored, and developed engineers; you set technical standards and drive consensus on architectural decisions
Track record of partnering cross-functionally with data science, ML, analytics, and product teams to deliver business outcomes through data infrastructure
Comfort with ambiguity and ownership mindset; you ship incrementally, measure impact, and iterate based on what you learn