A shortlist typically within 72 hours, usually contributing inside two weeks, and 30-day replacement support if the fit is wrong (terms in your agreement).
Shortlist typically in 72 hours · 30-day replacement support · Month-to-month
Databricks promises one platform for data engineering, analytics and machine learning. Done well, it delivers: reliable Delta tables, governed access through Unity Catalog and pipelines that recover from failure. Done badly, it turns into a pile of notebooks nobody can schedule, tables full of small files, and clusters left running over the weekend.
A senior Databricks engineer brings software engineering habits to data work: code in repositories, jobs instead of notebooks run by hand, and tables designed for how they will be queried.
Partitioning, joins, caching and reading query plans to find bottlenecks.
Merges, time travel, optimisation and vacuum handled properly.
Repos, tests, CI/CD and jobs instead of manual notebooks.
Access control, lineage and data sharing across teams.
Cluster policies, job clusters, serverless and auto-termination.
Feature engineering, MLflow and model deployment for data science teams.
Compute costs out of control. All-purpose clusters used for scheduled jobs, missing auto-termination and oversized nodes add up quickly.
Slow, fragile pipelines. Small files, skewed joins and no checkpointing make jobs slow and hard to rerun after a failure.
Governance gaps. Without Unity Catalog set up properly, it is hard to know who can see which data or where a number came from.
Every engineer we put forward has at least five years of hands-on experience. Within that, here is how the levels differ.
Senior Engineer (5–8 years). Builds Spark pipelines in PySpark or Scala, Delta Lake tables, Delta Live Tables or Workflows, and tests them. Comfortable tuning partitions, joins and cluster sizes.
Lead / Architect (8+ years). Owns the lakehouse design, medallion layers, Unity Catalog, CI/CD for jobs, cost governance and migrations from Hadoop or legacy warehouses.
Lakehouse builds. Bronze, silver and gold layers on Delta Lake with tested pipelines and clear ownership
Hadoop and legacy migrations. Moving Hive, Spark on-premise or warehouse workloads to Databricks
Unity Catalog rollouts. Central governance, lineage and access control across workspaces
ML platform support. Feature pipelines, MLflow tracking and model serving for data science teams
Week one. They review workspaces, clusters, job schedules, table layouts and where the money goes.
Weeks two and three. First pipelines or fixes delivered as version-controlled jobs, with quick wins on cluster policies and auto-termination.
Week four. A written plan covering table design, governance and cost, ranked by impact.
Running production from notebooks. Notebooks are good for exploration. Production needs jobs, repositories and CI/CD.
Ignoring file layout. Small files and missing optimisation make every query slower. Ask how candidates manage table maintenance.
Using all-purpose clusters for jobs. Job clusters or serverless compute are usually cheaper for scheduled work.
Rates depend on seniority, scope and engagement length. See the cost guide for market ranges, or tell us the role and we will give you a firm number.
Tell us the role and we will typically send a shortlist within 72 hours.
Tell Us the RoleTell us the role. Three to five profiles, typically within 72 hours.