Senior Data and Systems Architect with BMW since 2010: 9 years in engineering, 5 of them in data architecture, in complex, regulated environments.
When budget cuts hit, I replace slow, manual work — and the consultant spend behind it — with automation that cuts cost, speeds up delivery, and keeps working long after I've moved on.
- Cut daily operating effort from ~24 hours to 35 minutes for a 240-consumer data-model landscape after a staffing and budget cut, replacing two consultants and one internal position (~2.5 FTE, ~€350k/year).
- Delivered a metadata self-service capability in 20 workdays, closing a request that had been open for 6 years.
- Kept a mission-critical, always-on data model stable through a consultant budget cut with zero service interruption.
- Replaced status-chasing with a self-service dashboard showing exactly where each process stands, what's missing, and what to do next.
If your teams need to deliver faster under budget pressure without breaking long-term architecture, this is where I help.
- Stabilize always-on, business-critical data operations.
- Reduce overhead through AI-enabled automation and self-service.
- Give full, data-backed transparency into where every process stands, what's missing, and what happens next.
- Keep data models future-ready through governed evolution and release discipline.
Current focus: ownership of BMW's VSS-standardized data model, stewardship of petabyte-scale historized data, and delivery support for EU Data Act implementation across consuming systems.
In January 2026, budget cuts hit across the organization. Instead of pausing delivery, management repeatedly assigned me to the critical projects most at risk.
Result: Each project kept moving, and the slow, manual processes that had been holding those teams back were replaced with automation that keeps working without me — a sustainable fix, not a patch.
Why this matters for hiring teams:
- I get assigned the hardest problems when resources are tightest.
- My fixes hold up after I move on to the next project.
- Budget pressure becomes a trigger for lasting improvement, not just risk containment.
- You always know where a process stands, because I make status visible with data, not status meetings.
Result: Daily operating effort dropped from ~24 hours to 35 minutes for a 240-consumer data model, a 6-year-old self-service request was delivered in 20 workdays, and freed capacity was redirected to other budget-constrained projects.
Context: From January 2026, a critical data-model operation had to continue after staffing changes and consultant budget cuts. The prior setup required two external consultants and one internal position (~2.5 FTE).
Approach:
- System-level architecture thinking to remove structural bottlenecks.
- AI-driven implementation support with GitHub Copilot.
- Focused process and model refactoring, prioritizing self-service.
Why this matters for hiring teams:
- Delivery cost can be reduced through practical automation, not heavy process overhead.
- Speed can increase without trading off architectural quality.
- Teams get solutions that remain usable and maintainable over the long term.
- Every consumer can see live, data-backed process status instead of asking for updates.
When teams struggle with inconsistent data models across products, domains, or markets, I design a shared semantic model and governance path that enables reuse instead of rework.
Typical outcomes:
- Less model fragmentation and duplicate implementation
- Faster onboarding of new use cases
- Better cross-team collaboration through shared language
When product speed is blocked by tight coupling and unstable contracts, I define abstraction layers and interface rules that protect delivery teams from low-level volatility.
Typical outcomes:
- Clear producer-consumer boundaries
- Backward-compatible interface evolution
- Faster feature delivery without architectural debt spikes
When stakeholders from business, product, engineering, and governance are misaligned, I structure trade-offs and convert strategic intent into concrete architecture principles and delivery choices.
Typical outcomes:
- Higher decision quality under time pressure
- Transparent trade-offs and ownership
- Better alignment between roadmap and technical implementation
When systems must scale safely over years, I establish architecture guardrails for quality, testability, and maintainability without slowing teams down.
Typical outcomes:
- Consistent data and interface quality
- Reduced long-term change cost
- Better resilience in large legacy-heavy landscapes
- Led domain-wide data architecture and standardization initiatives at BMW Group.
- Designed middleware-based data standardization approaches for complex vehicle platforms.
- Delivered customer-facing intelligent functions from concept to series release.
- Closely aligned with COVESA: I guide BMW's COVESA liaison department and give implementation feedback to the VSS standard.
- Inventor on three patent filings in data-driven vehicle systems (sole inventor on two).
Detailed examples:
- Data architecture, governance, and lifecycle design
- Domain modeling, ontologies, and semantic consistency
- System and platform architecture for software-defined environments
- Business-to-technology translation and stakeholder alignment
More detail:
If you share a target role, I can provide a role-specific CV version aligned to your requirements.
Repository assets for tailored CV creation:
- Email: david.matzek@proton.me
- LinkedIn: linkedin.com/in/david-matzek-04114a10a