Most software and data platform failures come from hidden constraints, wrong assumptions and unintended trade-offs. We're now seeing AI scaling those risks faster.

I help leadership make the right data platform and backend decisions before architecture becomes expensive, margins erode, or systems outgrow their original design constraints.

When organizations seek perspective, I surface hidden constraints, clarify trade-offs, and define technically sound patterns that engineering teams can execute.

My work is grounded in 8+ years of experience across startups and high-throughput engineering environments. I currently work as a Foundational Data Platform / Backend Engineer across OLTP/OLAP systems, data flows and cloud infrastructure where reliability, latency, cost, clarity, and ownership matter.

Workshops & technical conferences:
https://www.linkedin.com/in/olivierbenard/

More on how I think about architecture and decision-making:
https://cloudframework.eu

• Terraform Modules for Cloud Platforms
  Reusable, versioned Terraform modules designed to make infrastructure ownership explicit and predictable.
  https://github.com/olivierbenard/gcp-terraform-modules

• Colocated Infrastructure & Deployment Template
  A reference structure (re-using the aforementioned exposed modules) showing how to co-locate infrastructure with services using Terraform, Terragrunt, and YAML — optimized for clarity, ownership, and low cognitive load.
  https://github.com/olivierbenard/infra-colocation-template

• Analytics Batch Pipeline Reference (Python + dbt + Airflow)
  Demonstrates how analytical workloads evolve from ad-hoc scripts into production-grade data platform pipelines.
  Focuses on layered modeling (raw → staging → marts), reproducibility, and trade-offs across ingestion boundaries, deduplication strategies, and state management (append-only vs constrained raw layers, hash vs business keys, MERGE vs full refresh).
  https://github.com/olivierbenard/analytics-batch-pipeline-reference

• CDC Data Platform Reference (DuckDB + Airflow)
  Explores correctness in event-driven data systems, including append-only ingestion, ordering guarantees, idempotency, and privacy boundaries (raw → staging_pii → staging → marts).
  Emphasizes auditability and late-binding transformations in CDC pipelines.
  https://github.com/olivierbenard/cdc-data-platform-reference

• CSV Ingestion Reference (TypeScript + Node.js)
  A small TypeScript reference project for backend/runtime patterns through a familiar data-ingestion use case.
  Reads CSV data, enriches records with ingestion metadata, applies deterministic transformations, and writes JSON output through a CLI and Dockerized runtime.
  Focuses on TypeScript fundamentals for platform work: typed contracts, runtime configuration, dependency injection, structured logging, testing with Vitest, linting, Docker/Compose execution, and the distinction between build-time and runtime dependencies.
  https://github.com/olivierbenard/csv-ingestion-ts

• Postgres Workload Reference
  Exploring PostgreSQL as a multi-workload platform (OLTP, time-series, vector, document).
  Focuses: how access patterns drive indexing strategy, cost and system boundaries.
  https://github.com/olivierbenard/postgres-workload-lab