Data Platforms
Hire senior Databricks engineers
Databricks is where lakehouse architecture meets production. Our seniors have run it at scale: Delta Lake tables, Unity Catalog governance, job clusters, and the Spark tuning that decides whether the platform is fast or expensive.
Shortlist of around 3 senior profiles in under 5 business days
- Seniority floor
- 7+ years owning production systems
- Time zone
- Full overlap with US Eastern to Pacific
- Shortlist
- Around 3 curated profiles in under 5 business days
- Engagement model
- Embedded in your team, no resellers or middlemen
- Pricing
- All-inclusive rate, around 35% below the US onshore equivalent
How we vet
What senior Databricks actually looks like
Spark internals: partitioning, shuffles, skew handling, and when photon actually helps
Delta Lake: ACID patterns, OPTIMIZE, Z-ordering, and CDC with MERGE
Unity Catalog governance across workspaces, plus medallion architecture in practice
Cost control: job vs all-purpose clusters, autoscaling policies, and spot strategy
Databricks questions, answered
What separates a senior Databricks engineer from a mid-level one?
A senior reads the Spark UI before touching code: they fix skew, partitioning and caching at the engine level, and design medallion architectures that stay maintainable as teams grow.
Can your engineers own both pipelines and ML workloads in Databricks?
Yes. Data engineers own the pipelines; ML and MLOps engineers own feature tables, training jobs and model serving on the same platform.
Do they work in PySpark or Scala?
Both exist in the bench, with PySpark plus SQL being the most common combination in US client projects.
Usually paired with Databricks
Keep reading about hiring for Databricks
Need senior Databricks on your team?
Tell us the context. A curated shortlist of senior profiles in under five business days, working in your time zone.