Director, Data Engineering

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What The Market Looks Like
Over the last six months, we've tracked 300+ senior-level data engineering postings across the US, with strongest concentration in California, Texas, and New York. These roles span IT services, financial services, technology, healthcare, and manufacturing—sectors increasingly dependent on reliable data infrastructure to power AI and analytics initiatives. Compensation for this cohort ranges from $220K to $440K annually. The strongest candidates bring 8+ years building and scaling data platforms at production scale, hands-on fluency with modern data stacks (cloud warehousing, streaming, orchestration), and the ability to translate between data architecture and business outcomes while growing and mentoring engineering teams.
Job Responsibilities
Own the design, architecture, and delivery of enterprise data platforms and pipelines that support analytics, AI/ML workloads, and real-time decision-making
Lead and mentor a team of data engineers, establishing technical standards, code review practices, and career development paths
Partner with data science, analytics, and business stakeholders to translate requirements into scalable, maintainable data solutions
Drive infrastructure modernization efforts—cloud migration, data warehouse consolidation, real-time data mesh architecture—and shepherd teams through technical transitions
Build data governance, quality, and security frameworks that balance innovation velocity with compliance and reliability
Establish monitoring, alerting, and incident response protocols to ensure platform reliability and performance at scale
Collaborate with engineering leadership to integrate data platform strategy with broader AI and business transformation roadmaps
Candidate Requirements
8+ years of hands-on data engineering experience, with at least 3 years in a lead or senior IC role owning platform architecture and team scope
Deep fluency with modern data stack tools—cloud data warehouses (Snowflake, BigQuery, Redshift), orchestration (Airflow, dbt, Dagster), and streaming platforms (Kafka, Spark)
Proven track record designing and scaling data infrastructure for production AI/ML and analytics workloads, including data quality and governance
Experience leading or growing a team of engineers; comfort mentoring, setting technical direction, and building culture
Strong systems thinking: ability to reason about trade-offs across performance, cost, maintainability, and security in platform design
Clear communication skills—translating technical architecture decisions for non-technical stakeholders and cross-functional peers

Work arrangement
Yes