Senior Solution Architect — Data & Integration
I design the data and integration backbone, build what runs on it, and get the organisation to adopt both.
Twenty-odd years designing the data, integration and streaming platforms that enterprises run on, almost never with a mandate behind me: 18–20 product teams onto one platform, three estates onto one event contract, a finance function that had to trust a number before it would use it. Systems in production, with the decisions written down where people could argue with them.
Track record
Four numbers from work that shipped. Each links to the case study behind it, including the argument.
18–20 teams, no mandate
Product teams consolidated onto one API platform by making the platform cheaper than staying put. ~€250–300k a year saved.
20+ data products, 30+ sources
Streaming data from 30+ source systems turned into 20+ domain-owned data products on Kafka. Schema rules let producers ship without a change board.
~30+ company codes
SAP Finance ledgers into Snowflake, adopted because Finance could reconcile the numbers itself.
~1.5B+ req/month
Federated cross-cloud API platform across AWS and Azure (Cloud Gateway), server-side errors under 0.03%
How I get it adopted
The design is usually the easier half. Whether anything ships is decided by the teams who each already have something that works, the function that has to trust a number, and the contract somebody signed four years ago. Three situations.
Twenty teams, no authority to move any of them
Asking for a mandate would have produced twenty exceptions. So the platform had to be cheaper for a team than staying put, and the first teams to move were the ones with the worst existing setup.
Three teams, three definitions of “done”
For SAP the job ended at “the events are on the broker”; for the cloud team it started at “we consume what is there”. Everything that mattered sat in the gap. I wrote the boundary down and took the unglamorous half myself.
A finance organisation with no reason to believe me
Explaining the pipeline changed nothing. A reconciliation Finance could run themselves against their own ledger did. Adoption followed the check.
What I build
Five areas, in the order I am usually hired for.
Data & lakehouse
Medallion lakehouses, contracts where the source hands off, and CDC pipelines that stay reliable, built so the numbers reconcile against the system people already believe.
Integration architecture
The backbone that lets enterprise systems talk: legacy ESB estates decommissioned wave by wave onto event-driven, API-led, domain-owned platforms.
Event-driven & streaming
Kafka and broker-based integration as the backbone of the estate, with schema evolution governed in the registry so producers can ship without a change board.
APIs & gateways
API platforms that scale across dozens of teams: gateway strategy, one security model, and the developer experience that gets them adopted.
AI & automation
AI integrated the way any other supplier system is: behind a contract, with an evaluation gate before release, and the model kept outside the runtime so it stays replaceable.
See the code behind it
Public repositories you can clone and run: the “active customer” question, the SAP↔Snowflake decision, the Fabric-or-Databricks decision, the modernization lab, the streaming platform and the identity service.