Lead customer-facing data engineering engagements from architecture through production handover. Build and optimize batch and streaming Lakehouse pipelines on Databricks using PySpark, SQL, and Delta Lake. Establish Medallion architecture conventions, implement data quality and automated testing, optimize Spark performance and platform costs, and mentor engineers. Partner with architects and explain technical trade-offs to customers.
About V4C
Requirements
Benefits
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 RoleLead 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
- Unlimited PTO
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary & AD&D)
- Family Leave (Maternity, Paternity)
- Short Term & Long Term Disability
- Training & Development
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