Lead Data Engineer, Databricks (Remote, US)

Overview

Lead the engineering workstream on Databricks Lakehouse engagements for V4C customers, owning pipeline delivery, quality and the engineers on the team.

What you'll do

• Lead engineering on customer engagements, from design through production handover
• Build and optimize batch and streaming pipelines using PySpark, SQL, and Delta Lake on Databricks
• Implement Medallion architecture across cloud ecosystems and set the layer conventions the team follows
• Own performance and cost: benchmark Spark jobs, tune SQL, right-size compute, and explain the trade-offs to the customer
• Implement data quality and validation as code, including Lakeflow expectations and automated testing in CI
• Mentor mid-level and junior engineers through code review, pairing, and design feedback
• Partner with the Data Architect on design decisions, and escalate when implementation reveals the design will not hold

What you'll need

• 7+ years of data engineering
• Experience leading projects or teams
• Strong proficiency in Python, SQL, Apache Spark, and Delta Lake
• Production experience building both batch and streaming pipelines at enterprise scale
• Hands-on experience with AWS, Azure, or GCP and its data services
• Experience with data modeling, Git, and CI/CD for data workloads
• Demonstrated experience optimizing Spark performance and managing platform cost
• Strong client communication skills, including the ability to explain technical trade-offs to a non-technical stakeholder

Nice to have

• Healthcare or life sciences experience
• Existing Databricks Certified Data Engineer Associate or Professional certification
• Hands-on experience with Unity Catalog, Lakeflow, or Databricks Asset Bundles
• Experience with dbt, Airflow, or equivalent orchestration and transformation tooling
• Experience migrating a legacy ETL estate to a modern cloud platform
• Consulting or client-facing engineering background

Details

• Remote, United States