Senior Data Engineer

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What the market looks like

We've tracked 25,400+ specialist-level data engineering postings across the US in the last six months, with strong hiring concentration in California, Texas, and New York. Compensation for this cohort typically lands between $150K and $290K annually. The strongest candidates combine deep expertise in modern data stack tooling—cloud warehouses, orchestration frameworks, and streaming platforms—with a track record of scaling pipelines under real production constraints. These engineers excel at balancing technical rigor with pragmatism: they understand data quality deeply but know when good-enough is the right call, and they communicate trade-offs clearly to cross-functional stakeholders.

Job responsibilities

• Design and build scalable data pipelines and architectures that ingest, transform, and serve data reliably at scale

• Own end-to-end data pipeline development, from requirements gathering through deployment and monitoring in production environments

• Partner with data scientists, analytics teams, and product engineering to understand data needs and translate them into robust technical solutions

• Lead data quality initiatives, including validation frameworks, testing strategies, and monitoring to catch issues before they reach downstream consumers

• Optimize data infrastructure for cost, latency, and reliability; drive architectural decisions that anticipate future scale

• Document data lineage, transformation logic, and operational runbooks to enable team knowledge sharing and reduce onboarding friction

• Mentor junior engineers on data engineering best practices and code review contributions to raise technical standards across the team

Candidate requirements

• 5+ years of professional data engineering experience, with demonstrated ownership of production data systems at scale

• Strong hands-on expertise with modern data stack tools—cloud data warehouses (Snowflake, BigQuery, Redshift), orchestration platforms (Airflow, dbt, Prefect), and distributed processing frameworks

• Proven ability to design and implement data architectures that balance performance, cost, and maintainability in high-throughput environments

• Solid software engineering fundamentals: version control, testing, code review discipline, and ability to write production-grade code in Python, Scala, SQL, or similar languages

• Experience collaborating with stakeholders across data science, analytics, and engineering to scope requirements and communicate technical constraints

• Track record of shipping production systems and maintaining them through operational incidents and scaling challenges