Build and optimize production batch and streaming data pipelines on Databricks using PySpark, SQL, Spark, and Delta Lake. Responsibilities include implementing Medallion architecture, deploying cloud data resources, automating validation, tuning performance, structuring enterprise datasets, and troubleshooting production issues. The role requires end-to-end ownership of enterprise data platforms, legacy warehouse migrations, data modeling, CI/CD, DevOps, and client-facing collaboration.
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 Databricks Engineer, you will build and optimize production pipelines on the Databricks Lakehouse, 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.
PositionDatabricks Engineers build the pipelines enterprise customers run their business on. You will work under a senior engineer or architect, taking a defined piece of an engagement and owning it end to end: ingesting from systems that were not designed to be ingested from, transforming data through Medallion layers, and getting it to a state analysts and ML workloads can actually use. You will be client-facing — standups, working sessions, occasionally explaining to a stakeholder why something is harder than it looked. This is where most of our engineers start, and the certification roadmap is built around moving you up from it.
Responsibilities
- Build and maintain batch and streaming ETL pipelines using PySpark, SQL, and Delta Lake on Databricks
- Implement Medallion architecture layers
- Deploy and manage data resources on AWS, Azure, or GCP
- Write and automate data validation and quality checks
- Tune Spark jobs and SQL queries for performance and cost
- Structure and clean enterprise datasets for downstream analytics and AI workloads
- Troubleshoot pipeline failures and data quality issues, including in production
Requirements
- 8+ years in data engineering/architecture or analytics platform development
- Demonstrated ownership of end-to-end architecture for at least two enterprise-scale data platforms, from design through production
- Deep experience migrating legacy data warehouses or ETL estates to a modern cloud platform (Teradata, Informatica, Hadoop, Netezza, or equivalent)
- Strong proficiency in Python and SQL; working knowledge of Apache Spark and distributed processing fundamentals
- Experience with data modeling across dimensional, normalized, and Lakehouse patterns, and the judgment to know when each applies
- Experience with CI/CD and DevOps practices for data platform deployment
Preferences
- Healthcare or life sciences experience preferred but not required
- Databricks Certified Data Engineer Professional or a Platform Architect
- Hands-on Databricks experience at scale: Unity Catalog, Delta Lake, Lakeflow, Asset Bundles
- Experience with MLOps or productionizing machine learning workloads
- Prior professional services, consulting, or customer-facing architecture background
- Experience contributing to pre-sales scoping or technical proposals
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
- Remote/WFH
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