Consulting for organisations
Databricks Consultants and Data Engineers
Databricks consultants design, build and tune data platforms on the Databricks Data Intelligence Platform. Whether you are starting a lakehouse, moving off a legacy warehouse or bringing an existing workspace under control, you get hands-on engineering rather than slideware.
What the role does
They turn raw data into reliable, governed tables your analysts and data scientists can depend on — designing the layout, writing and scheduling the pipelines, setting up access and lineage, and keeping compute costs in check as usage grows.
Typical responsibilities
- Design lakehouse architecture with Delta Lake and medallion (bronze, silver, gold) layers
- Build batch and streaming pipelines with Spark, Lakeflow Declarative Pipelines and Jobs
- Set up Unity Catalog for governance, lineage and fine-grained access control
- Migrate workloads from on-premises warehouses, Hadoop or other cloud platforms
- Tune clusters, SQL warehouses and jobs for performance and cost
- Enable machine learning and generative AI workloads with MLflow and Mosaic AI
- Automate deployment with Databricks Asset Bundles and CI/CD
Skills
- Apache Spark (PySpark and Spark SQL)
- Delta Lake
- Unity Catalog
- Lakeflow and Databricks Jobs
- Databricks SQL
- MLflow
- Databricks Asset Bundles
- Azure, AWS or Google Cloud
Relevant certifications
- Databricks Certified Data Engineer Associate
- Databricks Certified Data Engineer Professional
- Databricks Certified Data Analyst Associate
- Databricks Certified Generative AI Engineer Associate
- Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
Each proposed engineer’s own certifications are listed on their profile, so you can check them before deciding.
Engagement options
Choose the shape that fits the work. Scope, working hours and commercials are agreed with you after a scoping call.
Single engineer
One specialist joins your team for a defined scope or a continuing role, working inside your tools and processes.
Small pod
Two or more engineers with complementary skills — for example an architect alongside data engineers — delivering as one unit.
Project-based
A scoped outcome with agreed milestones, deliverables and acceptance criteria, rather than open-ended time.
Ongoing support
Continued maintenance, monitoring and enhancements once your platform or solution is in production.
Frequently asked questions
Request a consultant
Tell us who you need
Share the role and context. We reply to arrange a short scoping call and then share suitable engineer profiles for you to review.
Prefer to talk it through?
Message our team on WhatsApp with a short outline of what you need.