Databricks Engineer (Remote, US)

V4C.ai

7h•$100,000 - $120,000•Remote

About The Position

V4C is a pure-play Databricks partner that has earned more than 850 Databricks certifications across more than 325 customers and was named Databricks' 2026 Partner of the Year (C&SI Emerging). Databricks Engineers build and optimize production pipelines on the Databricks Lakehouse, working directly with customers and often with Databricks field teams. In this role, you will work under a senior engineer or architect, take ownership of a defined part of an engagement from start to finish, ingest data from source systems, transform it through Medallion architecture layers, and prepare it for analytics and machine-learning workloads. The role is client-facing, including standups, working sessions, and explaining technical challenges to stakeholders. V4C provides technical hires with an in-house Databricks certification roadmap taught by Databricks-approved trainers and a dedicated Employee Success Manager focused on their experience.

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, such as 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.

Nice To Haves

Healthcare or life sciences experience.
Databricks Certified Data Engineer Professional certification or Platform Architect certification.
Hands-on Databricks experience at scale, including Unity Catalog, Delta Lake, Lakeflow, and 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.

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.
Own a defined part of a customer engagement end to end, from data ingestion and transformation through preparing data for use.