Databricks Engineer (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 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.

Position

Databricks 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