Data, models, and the work between
I lead data and AI delivery for enterprise clients, mostly lakehouse and data-mesh architectures on Azure Databricks and Microsoft Fabric, plus GenAI that has to pass a governance review. Twelve years of it, across financial services, energy and transport. I built a remote team from 2 engineers to 9 consultants. What I care about most is the unglamorous part: the roadmap, the guardrails, and whether the number a business acts on is right.
Recent articles
Analytics Adoption is Decided Before the Build
A dashboard that did everything right went unused. The difference between analytics that gets adopted and analytics that gets ignored is settled before delivery starts.
Who Chases the Data Quality Fix
Data teams can define the rules, build the gates and publish the dashboards, and the fixes still stall. What changed when the chasing moved to the owner of the numbers.
What I work on
- Databricks
- Microsoft Fabric
- Snowflake
- BigQuery
- Amazon Redshift
- AWS Glue
- Apache Spark
- Kafka
- Airflow
- Python
- SQL
- Azure Data Factory
- Terraform
- Azure AI Foundry
- Vertex AI
- Semantic Kernel
- MLflow
- RAG
- AI agents
- Unity Catalog
- Microsoft Purview
- Delta Sharing
- Azure DevOps
- GitHub Actions
- Power BI
- Looker