Databricks Data Engineer

About The Role

You'll build and run lakehouse platforms on Databricks for our North American and European clients — ingestion pipelines, Delta Lake architectures, transformation layers and the governance around them. This is client-facing engineering: you'll present your work in the client's standup, own outcomes end to end, and work with modern data stacks in production, not in a sandbox.

What you'll do

• Design and build batch and streaming pipelines with Spark, Delta Live Tables and Delta Lake
• Implement medallion architectures with Unity Catalog governance
• Migrate legacy warehouses (Redshift, Snowflake, on-prem) to the lakehouse
• Optimize cluster configuration and pipeline cost — and explain the trade-offs to clients
• Work directly with client engineering teams in US and European timezones (structured overlap hours, not night shifts)
  
  

What we're looking for

• 3–6 years in data engineering, with at least one year of hands-on Databricks or serious Spark experience
• Strong Python and SQL — you write code that other engineers review and reuse
• Experience running pipelines in production: monitoring, incident handling, backfills
• Clear written and spoken English — you'll communicate directly with clients
• Databricks certification is a plus; if you don't have it, we'll pay for you to get it
  
  

What you get

• Databricks, AWS and Retool certification costs and prep time covered
• Direct client exposure and ownership from your first month
• Structured timezone overlap — evenings are for occasional calls, not a permanent shift
• A small, senior team where your work is visible
  
  

To apply, email your CV to [Confidential Information] with the subject Application: Databricks Data Engineer.