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About

I'm a Data Architect building privacy-first data foundations for HealthTech and analytics products.

My work focuses on designing scalable data systems that handle sensitive information — PII, PHI, health records — responsibly. I build data models, governance frameworks, and ETL pipelines that turn raw signals into actionable insights while maintaining strict privacy controls, auditability, and data quality.

Background

I started in computer science and software engineering, then moved through ML systems and data pipelines before focusing on data architecture and governance. The pattern I kept seeing was that organizations needed strong data foundations before they could do anything meaningful with analytics or AI — and that privacy couldn't be bolted on after the fact.

I completed Master's-level coursework at the University of Helsinki in Trustworthy ML, Big Data Platforms, and Software Architecture, which shaped how I think about building data systems that are both technically sound and aligned with privacy and trust requirements.

Most of my work involves:

  • Data modeling & schema design — multi-tenant architectures, longitudinal data, relational schemas
  • Data governance & access control — privacy policies, audit trails, role-based permissions
  • ETL & data pipelines — transforming raw data into structured, queryable insights
  • Privacy-by-design — building compliance (LOPD, GDPR) into the data layer from day one
  • Production ML systems — RAG pipelines, forecasting, evaluation workflows

Current Focus

I'm leading data architecture at SaliHub, designing the data foundation for a preventive health ecosystem. This means building the core data model, governance framework, and pipeline infrastructure to support scalable, privacy-compliant health analytics.

Values

  • Privacy by default. Sensitive data deserves careful handling from the start, not as an afterthought.
  • Measure data quality. If you can't trust the data, you can't trust the decisions it drives.
  • Simplicity scales. Complex systems fail in complex ways.
  • Governance enables speed. Clear rules and good structure let teams move faster, not slower.

If you're working on something interesting in data architecture, HealthTech, or privacy-aware systems, I'd like to hear about it.