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What I do

Services

One focus: making the Databricks Data Intelligence Platform on Azure deliver. Reliable pipelines, clear governance, and data people actually use.

01

Lakehouse data engineering

The foundation of every data platform is a reliable Lakehouse. I build a medallion architecture on Azure Databricks with Delta Lake and Lakeflow Declarative Pipelines: idempotent ingestion, tested transformations and orchestration that lets you sleep at night. Performant and cost-conscious.

  • Medallion architecture (bronze / silver / gold) on Delta Lake
  • Lakeflow Declarative Pipelines (DLT) and job orchestration
  • Incremental ingestion with Auto Loader and CDC
  • Performance and cost tuning (clusters, Photon, partitioning)
Delta LakeLakeflowAuto LoaderPhotonADLS Gen2 More about this service →
02

Unity Catalog & governance

Without governance a Lakehouse quickly turns into a swamp. Unity Catalog gives you a single place for access control, lineage and quality, across every workspace. I capture the setup as Infrastructure as Code with Terraform, so everything is reproducible instead of disappearing into click-ops in a UI.

  • Unity Catalog setup: catalogs, schemas, fine-grained grants
  • Infrastructure as Code with Terraform (Databricks provider)
  • Automatic lineage and audit from source to dashboard
  • Data quality and secure sharing with Delta Sharing
Unity CatalogTerraformLineageDelta Sharing More about this service →
03

AI/BI, Genie & reporting

Data only delivers value when people get answers from it. With Databricks AI/BI Dashboards and Genie spaces I let users ask questions in plain language, grounded in a reliable semantic model. And where Power BI is the standard, I connect it cleanly to the Lakehouse.

  • AI/BI Dashboards on governed Lakehouse data
  • Genie spaces for natural-language analytics that hold up
  • A clear semantic / metric model as the source of truth
  • Power BI integration via Databricks SQL
AI/BIGenieDatabricks SQLPower BIMetric Views More about this service →
04

Data-driven apps

Sometimes a dashboard isn't enough and you need a real application: a data-entry screen, an approval flow or a data product. With Databricks Apps I build and host those applications directly on the platform, with Lakebase (managed Postgres) as a fast operational layer that integrates directly with the Lakehouse.

  • Databricks Apps: build and host next to your data
  • Lakebase (managed Postgres) for operational and transactional data
  • Secure access through Unity Catalog, no separate data copy
  • From internal data product to customer-facing application
Databricks AppsLakebasePostgresUnity Catalog More about this service →

Frequently asked questions

Does dataskippr only work with Azure Databricks?
Yes, that focus is deliberate. By specialising in the Databricks Data Intelligence Platform on Azure (Lakehouse, Unity Catalog, AI/BI and Genie) I bring depth instead of a little of everything. The surrounding Azure services such as ADLS Gen2, Entra ID and networking naturally come with it.
Do you also do data science and model training?
No. My focus is data engineering and making data usable through AI/BI and Genie, not developing machine-learning models. I make sure your data platform and governance are solid, so data scientists have a good foundation to work on.
What is Genie and why would I use it?
Genie is the natural-language layer of Databricks AI/BI: users ask questions in plain language and get answers grounded in your governed Lakehouse data. It works best on top of a well-modelled semantic layer, exactly what I help put in place, so the answers actually hold up.
Can you build real applications, not just dashboards?
Yes. With Databricks Apps and Lakebase (managed Postgres) I build interactive, data-driven applications directly on the platform. Think data-entry screens, approval flows or data products, with the same Unity Catalog governance as the rest of your Lakehouse.
We already use Power BI. Does that fit?
Absolutely. Power BI connects cleanly to the Lakehouse via Databricks SQL. Unity Catalog stays the source of truth for access and lineage, while your existing reporting keeps working.
What kind of engagements are possible?
Anything from a Lakehouse architecture or governance review to building pipelines end to end, rolling out AI/BI and Genie, or building a data-driven app. Both short advisory work and longer hands-on engagements are possible.

Ready to set course?

A no-obligation conversation about your Azure Databricks, Lakehouse or AI/BI challenge. I'm happy to think along.

Book a call