I design and rebuild data platforms for organizations that have outgrown their dashboards and spreadsheets. Migrations, architecture and analytics at scale — told through real use cases and battle-tested patterns.
Concrete patterns for streaming, lakehouse and cloud-native platforms — not vendor slides.
Turn your Oracle OLTP into an always-fresh analytics plane. Streaming CDC with GoldenGate landing into Iceberg tables or BigQuery, with replayable history, schema evolution and low-latency consumption.
Moving from rigid warehouses to Iceberg/Delta/Hudi-backed platforms, while keeping BI stable and giving data teams room to evolve.
How to stop every team from redefining revenue. Semantic layers, contracts and versioned metrics as a first-class product.
Real-world scenarios from migrations, platform rebuilds and analytics modernization.
A retail organization running on decade-old ETL and hand-crafted reports. We redesigned the data model, migrated to a cloud warehouse, and moved from opaque jobs to observable pipelines with governed access.
Multiple CRMs, billing systems and support tools disagreed on who the customer was. We aligned semantics, built a golden record, and gave sales, support and finance a shared, consistent view.
Dozens of dashboards, contradictory numbers and “Excel as truth”. By simplifying the semantic layer and enforcing data contracts, we reduced executive dashboards to a handful of trusted views.
The part no dashboard shows and no sprint plans acknowledge.
Modern systems don’t fail because of technology. They fail because debt accumulates silently inside pipelines, semantics, metrics, and ownership.
Explore the index →Long-form field notes on data architecture, migrations and analytics in real organizations.
Migrations expose everything you thought was “just technical”. Ownership, power, culture and the real shape of your decision-making show up in your data flows long before they show up on a slide.
Read Part 1 →No migration starts with a Jira ticket. It starts when leadership is willing to change how decisions are made and who owns which numbers. Without that movement, the project is just an expensive backup.
Read Part 2 →Underneath the narrative, there are tables, constraints and dirty history. This part goes into the technical debt, reconciliation battles and quiet cleanup work that determines whether a migration actually lands.
Read Part 3 →Who’s behind the keyboard — and what this site is here to do.
I’m a senior data engineer and data architecture specialist with more than a decade inside real systems — data warehouses, cloud migrations and analytics platforms in retail, telco, insurance and public organizations.
I’ve worked with very different stacks and very different styles of leadership: from tightly controlled legacy environments to fast-moving teams that ship before they fully understand what they’ve built. Along the way, I’ve seen migrations that landed well, and many that created more debt than they removed.
This site is not a tutorial library. It’s a collection of field notes from projects that actually ran in production: what broke, what survived and what turned out to matter much more than the choice of tool.
If you’re planning a migration, rebuilding a data platform or trying to make your organization trust its own numbers again, you’ll probably find something useful here.
This is where architecture meets politics, and data meets human behavior.
Open to EU remote B2B contracts, data platform design, and architecture advisory for teams that are serious about their data.
If you’d like to discuss a specific use case or platform migration, send a short outline and we can see if it’s a good fit.