I help early-stage founders build data systems that scale, both technically and securely. With 10+ years of experience scaling data platforms and deep expertise in GDPR, customer data privacy, and AI compliance, I advise on architectural and regulatory decisions that determine whether you scale sustainably or hit costly bottlenecks during growth and fundraising.
What you get from working with me:
Experience:
I've built data infrastructure processing billions of records and managed engineering teams through rapid scaling. I've also seen what happens when founders don't plan data architecture early, resulting in processing bottlenecks that block analytics, data quality problems that erode trust, manual data processes that consume engineering time, and technical debt that requires €750k–€3.75M rebuilds post-Series A. I understand both sides, scaling systems under pressure, and building governance that impresses investors.
I'm genuinely excited about helping founders build data infrastructure that scales and holds up under investor scrutiny. Most treat data architecture as "something to figure out later". I help you make the right calls now. And most treat privacy as a compliance checkbox. I help you see it as a moat. Founders who build privacy-first from the start gain investor confidence and customer trust that competitors can't easily replicate.
Architecture skills that I bring:
Scalable system design: building pipelines that handle 10x data growth without breaking
Data quality and validation: preventing the quality dilution that emerges as you scale
Pipeline architecture: modular, maintainable systems (not monolithic nightmares)
Real-time analytics: designing for insights, not just historical reporting
Data team scaling: structuring your teams so you're not bottlenecked on one person
Privacy & compliance skills that I bring:
GDPR fundamentals: Customers' data rights, data processing agreements, DPIAs
Privacy-by-design: embedding compliance into architecture from day one
AI-specific GDPR challenges: Customers' rights for AI model training data, Training on Public data, right to explanation
Data governance frameworks: making privacy a competitive advantage, not a burden
Best fit: pre-seed to Series A, handling customer data and scaling analytics, planning to raise within 12–18 months, wanting to position data infrastructure as a strength, not a liability.
