Senior Data Engineer

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What the market looks like

We've tracked 34,500+ specialist-level data engineering postings across the US in the last six months, with heavy concentration in California, Texas, and New York. Compensation for this cohort ranges from $150k to $290k annually, depending on experience and specific technical depth. The strongest candidates in this market combine deep hands-on expertise in modern data stack tools—cloud platforms, streaming systems, and warehouse architecture—with proven ability to own data infrastructure end-to-end. Organizations are looking for engineers who can move fast, communicate trade-offs clearly to stakeholders, and scale systems that support AI/ML workloads.

Job responsibilities

• Design, build, and maintain scalable data pipelines and infrastructure to support analytics, BI, and ML teams

• Own data warehouse or lakehouse architecture decisions and implementation, including schema design and optimization

• Develop and optimize ETL/ELT processes, ensuring data quality, lineage, and SLA compliance

• Partner with analytics, data science, and product teams to understand data requirements and translate them into reliable infrastructure

• Build and maintain monitoring, alerting, and observability systems for data pipelines and platform health

• Lead code reviews, document architectural decisions, and mentor junior engineers on best practices

• Drive incremental improvements to data platform performance, cost efficiency, and developer experience

Candidate requirements

• 5+ years of hands-on data engineering experience, with demonstrated expertise in building and operating production data systems

• Strong proficiency with cloud data warehouses (Snowflake, BigQuery, Redshift) or data lakes, and modern ETL/ELT tools (dbt, Airflow, Spark)

• Track record shipping data pipelines and infrastructure that measurably improved team velocity or data quality for downstream consumers

• Solid grasp of data modeling, SQL optimization, and performance tuning in large-scale systems

• Clear communicator who can articulate technical trade-offs and work effectively across engineering, analytics, and business teams

• Experience working in or supporting AI/ML workflows and understanding of data requirements for training and inference pipelines is a plus