Services
Four stages of the same work: understanding the question, deciding where the data should live, building the pipelines that get it there, and turning it into something a business can actually use. Most projects start at one stage and grow into the next, not all four at once.
Data strategy
Most data problems start as a business question, not a technical one. Before a pipeline gets built, the real question needs sharpening: what decision is this meant to support, which existing systems already hold pieces of the answer, and whether the answer is worth the engineering it would take to get there. This leans on hands-on stakeholder work, not a slide deck.
- Stakeholder interviews to identify and interpret the right data sources
- Scoping a data initiative: what is feasible, what isn't, and why
- Translating a business question into a concrete, buildable plan
- Business/process analysis, drawing on prior operational and startup experience
Data platform architecture
Architecture decisions made early are expensive to undo later: which tools to standardise on, how to structure the warehouse, and how raw data becomes something a dashboard or a model can actually query. This covers Medallion-style layering, Kimball-based data marts, orchestration setup, and planning a migration off a legacy system. Having worked on several enterprise-scale data platforms, I've seen firsthand where these architecture decisions go wrong.
- Architecture decisions: tool selection, warehouse structure, orchestration setup
- Medallion architecture and Kimball-based data mart design
- Migration planning from a legacy platform to a modern data warehouse
- Repository structuring and CI/CD pipeline setup for a data platform
Data engineering
This is the hands-on core: pipelines that pull from APIs, SFTP, and message queues, normalising semi-structured JSON, XML, and Parquet data into a consistent model, and keeping it reliable with data quality checks and monitoring. Built on Snowflake, dbt, Airflow, Azure Data Factory, and Matillion across several production data platforms.
- ETL/ELT pipeline development for structured and semi-structured sources (APIs, SFTP, Kafka)
- Data migration execution between platforms (for example, Oracle to Snowflake)
- Data quality checks, monitoring, and metadata logging
- dbt modelling, stored procedures, and pipeline automation
Analytics & products
The last step is making the data usable: dashboarding for internal stakeholders, or a full data product. This covers dashboard design and build, data marts tuned for reporting performance, and quality-metrics reporting so the people using a dashboard know how much to trust it.
- Dashboard design and development
- Data marts tuned for reporting and BI consumption
- End-to-end data products, from source to delivered dashboard
- Quality-metrics and completeness reporting alongside the data itself