From platform foundations to trusted data products.

Cloud data platform modernization

Move pipelines and workloads toward maintainable cloud and lakehouse foundations using shared delivery patterns, infrastructure as code, observable workflows, and explicit ownership.

Relevant experience: more than 50 pipelines modernized with AWS, Databricks, Delta Lake, PySpark, Unity Catalog, SQL, and Terraform.

Governance built into delivery

Make lineage, data quality, access policy, contracts, and secure delivery part of the platform itself. This is especially useful when multiple systems or teams must keep operating during change.

Relevant experience: controlled Atlas and SAP coexistence using Databricks, DLT, Delta, Kafka, Terraform, shared Commerce models, and quality controls.

Production pipelines and analytics foundations

Build reliable ingestion, transformation, semantic models, and access controls so operational and analytical users can work from consistent data.

Relevant experience: finance analytics across 18 countries and 12 currencies using PySpark, dbt, Delta Lake, Power BI, Kyriba rates, and country-level row security.

Evidence-first AI data systems

Prepare transparent retrieval and structured-data foundations for AI products, with clear provenance and a deliberate boundary between database answers and document-grounded answers.

Public example: MonÉlu turns French parliamentary archives into tested dbt marts, a FastAPI service, and an AI assistant that exposes its evidence.

Who this is for

This portfolio is relevant to teams hiring for cloud data engineering, modernizing a data platform, improving governance and reliability, or exploring evidence-backed AI on top of trusted data.

It describes professional capabilities and selected work. It is not a self-service software product, public API, or published rate card.