Senior Data Engineer
United States · Hybrid
Learn more about our Data & Analytics Recruitment solutions.
Axial Search recruits for AI transformation roles across North America, building long-term relationships with leaders across every industry vertical. Apply today to express your interest in this position and others like it. Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.
What the market looks like
Data engineering remains the quiet backbone of every serious AI program. We track several thousand senior data engineering postings in the US annually, with hiring concentrated across product-led technology firms, data-intensive enterprises, and consultancies. Mid-senior compensation generally lands in a $145K–$230K base band, and around one in eight roles is fully remote. Cloud warehouse work (Snowflake, Databricks, BigQuery) is near-universal, and streaming and real-time patterns are increasingly the differentiator between mid and senior candidates.
Job responsibilities
Design, build, and operate data pipelines that power analytics, ML, and GenAI products
Own the reliability, quality, and observability of critical data assets
Partner with data scientists, ML engineers, and analysts on schema, tooling, and data contracts
Drive architectural decisions around warehouse, lakehouse, and streaming patterns
Mentor junior engineers and set the team's standards for data engineering practice
Advocate for the right balance of speed, cost, and long-term maintainability
Contribute to the roadmap for internal data platform capabilities
Participate in on-call rotations where the team maintains critical production data services
Candidate requirements
5+ years of data engineering experience with strong SQL and Python (or Scala)
Hands-on experience with modern warehouse or lakehouse stacks — Snowflake, Databricks, or BigQuery
Strong background in orchestration (Airflow, Dagster, dbt) and streaming (Kafka, Flink, or Kinesis)
Comfort with cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code
Strong debugging instincts across pipeline, infrastructure, and data-quality issues
Experience partnering with ML and analytics teams on shared data assets
