About.

Long biography

Data Solution Architect on Google Cloud. 25 years from software engineering into data warehousing, big data and cloud. For the last four years I’ve designed and led the migration and modernization of the enterprise data platform at LC Waikiki, a fashion retailer with 1,000+ stores in 50+ countries plus e-commerce: ~80 TB of analytical data, 100+ source systems, thousands of business users.

• Data platform architecture on GCP: BigQuery, Dataflow (Apache Beam, Java), Pub/Sub, Dataform, Cloud Composer, Cloud Storage, Cloud Run. Layered warehouse design (Landing / Staging / Core / Data Mart), dimensional modeling, ELT with versioned SQL, CI/CD and quality assertions across 3,000+ tables; catalog and lineage across 100+ sources.
• Cloud migration and cost engineering: designed and led the move of our SQL Server + Cloudera (15-node) warehouse to BigQuery: ~75% lower infrastructure cost, ~$550K saved per year, batch ~7x faster. A GA4 redesign cut monthly cost from $9K to $3K and took the heaviest queries from 46 minutes to 8 seconds.
• Streaming: the org-wide real-time pattern is Pub/Sub to Dataflow (Beam) to BigQuery via the Storage Write API, plus a reverse pipeline from BigQuery to SQL Server for operational systems.
• GenAI and AI governance: LLM agents with evaluation frameworks (golden, trajectory, injection, drift) for data onboarding, keeping production deterministic and auditable. I also write on AI accountability: who owns, stops and audits an AI decision.

Before GCP I built the SQL Server side of the same platform: Turkey’s first FastTrack data warehouse, a 15 TB SSAS OLAP estate, a metadata-driven SSIS framework; two were published by Microsoft as customer success stories.

I’ve also run the organisation: I built LC Waikiki’s first data engineering team and later led an 18-person data organisation with a $1M+ budget through the BigQuery migration. In 2024 I chose to step out of line management into a hands-on architect role, because the platform’s next phase (streaming, GenAI-enabled data products, FinOps at scale) needed design authority next to the code.

Today I set the target architecture, the modelling and quality standards and the reference implementations the data engineering and analytics teams build against, and run the architecture reviews and ADR process that keep them aligned.