Lead Data Engineer, Databricks (Remote, US)

Descripción del empleo

About V4C

V4C is a pure-play Databricks partner, and in under two years we have earned 850+ Databricks certifications across 325+ customers and been named Databricks' 2026 Partner of the Year (C&SI Emerging).

As a Lead Data Engineer, you will lead the build of production Lakehouse pipelines on customer engagements, working directly with customers and often Databricks' own field teams. Every technical hire here gets a certification roadmap built for them and taught in-house by Databricks-approved trainers, plus a dedicated Employee Success Manager whose only responsibility is your experience at V4C.

The Role

Lead Data Engineers are the technical backbone of V4C delivery. You lead the engineering workstream on an engagement — translating an architecture into working pipelines, setting the patterns the team follows, and owning the quality of what ships. You will work across batch and streaming, on messy enterprise source systems, under real deadlines, and in front of the customer explaining what you built and why. The difference from a Data Engineer is ownership: you are accountable for a workstream and the engineers on it.

Responsibilities

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

Requirements

7+ years of data engineering plus 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

Preferences

Healthcare or life sciences experience preferred but not required
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

Benefits