Description

Available for weekend and short-term data engineering missions — remote from Switzerland.

I'm a Senior Data Engineer and Technical Team Lead with 5+ years of experience building production-grade data platforms across healthcare, e-commerce, and fintech. Currently employed full-time, I take on focused freelance projects on evenings and weekends.

What I deliver on short engagements:

— Data platform architecture and design reviews (GCP, Azure, AWS, Databricks)

— dbt project setup, modeling, and optimization

— Pipeline development with Dagster, Airflow, or Azure Data Factory

— Cloud cost audits and infrastructure optimization (proven 50% cost reductions)

— AI/LLM integration into data workflows (Claude, Vertex AI, OpenAI)

— Data mesh implementation and domain modeling strategy

— Streaming pipelines (Pub/Sub, Kafka, Flink)

— Knowledge graph design (Neo4j)

Tech stack: Python, SQL, dbt, Databricks, GCP (BigQuery, Pub/Sub, Cloud Functions), Azure (Data Factory, Synapse), AWS, Terraform, Kubernetes, Docker, Dagster, Fivetran, Airbyte, Looker, Power BI, Streamlit.

I've built a SaaS product solo (ApplyCH), scaled 10TB+ data domains, and led distributed teams of up to 10 engineers. I bring senior-level judgment to every engagement — no hand-holding needed.

Trilingual: English, French, Turkish. Swiss-based, fully remote.

Ideal projects: 1-5 day sprints, architecture reviews, proof-of-concepts, pipeline migrations, or firefighting production data issues over a weekend.

Domaines d’expertise

Langues

Anglais
Bilingue ou natif
Français
Bilingue ou natif
Turc
Bilingue ou natif

Préférences en matière de lieu de travail

En télétravail uniquement

Travaille majoritairement à distance

MEDGATE
TECHNICAL TEAM LEADER – ANALYTICS
SECTEUR MÉDICAL
janvier 2026 - Aujourd'hui (5 mois)
Bâle, Suisse
Delivery Turnaround: Inherited a reactive team stuck in permanent hotfix mode. In 3 months, reduced hotfix incidents to near zero, transformed the team into a confident, delivery-focused unit with clear ownership and predictable output.
Platform Boundaries & Collaboration: Defined the analytics platform's scope, pushed operational duties to their corresponding teams. Established cross-team collaboration with source system owners (nonexistent before), driving data quality improvements at the source.
CI/CD Overhaul: Migrated to full trunk-based deployment. Rebuilt the entire CI/CD pipeline with proper testing environments, preprod report generation, and Power BI dashboard creation in defer mode.
AI-Augmented Code Review: Built an automated Claude PR review system with a custom PII anonymizer ensuring no patient data reaches external APIs — fully compliant, organization-approved, running at minimal cost.
SWISS MARKETPLACE GROUP (SMG) – Autoscout24,
SENIOR DATA ENGINEER
E-COMMERCE
octobre 2022 - janvier 2026 (3 ans et 3 mois)
Zurich, Suisse
Founding Data Mesh Engineer: First engineer to operate under a Data Mesh model at SMG. Defined the architectural blueprint and domain onboarding guidelines adopted by all subsequent domains — built the playbook for scaling data ownership across the org.
Domain Owner — 10TB+ Customer Data: Owned the Demand domain end-to-end — 10TB+ of customer data (GA3, GA4, custom tracking pixels) into analytics-ready marts in BigQuery. Worked daily with a 30-person cross-functional squad (devs + PO).
Cloud Cost Optimization (−50%): Consolidated 10+ K8s workloads into a single Dagster orchestration layer (−30%). Built a Python cost monitoring app for Finance (−20% more) — 50% total savings.
Modern Data Stack & Governance: Deployed Dagster on Kubernetes with dbt, Fivetran, Airbyte across 20+ sources. Terraform IaC, real-time Pub/Sub ingestion. Atlan governance with data contracts and full CI/CD.
AI & BI Products: Partnered with Google to deploy AI assistant (Vertex AI / Gemini). Built HR tool (OpenAI). Looker dashboards + Streamlit cost monitoring apps.
ALPHA10X
DATA ENGINEER
CONSEIL & AUDIT
septembre 2021 - septembre 2022 (1 an)
Aix-en-Provence, France
Databricks Lakehouse Platform: Architected and delivered a terabyte-scale enterprise data platform on Azure Databricks using Delta Lake for curated data models. Implemented batch and streaming architectures, enabling enterprise-wide analytics and data governance with ACID compliance.
Real-Time Streaming: Engineered streaming pipelines using Apache Flink for real-time processing, reducing data ingestion latency from 24 hours to under 60 minutes — enabling near-real-time business intelligence.
Knowledge Graph: Developed a billion-node knowledge graph in Neo4j, supported by custom Python microservices for data normalisation, enrichment, and entity resolution at enterprise scale.
Data Access & Enablement: Built a comprehensive data access layer with a Delta table backend and data science catalogue workspace, providing seamless access for analysts and data scientists.
Schema Normalisation: Created Python-based schema normalisation tools for heterogeneous data sources, ensuring data quality, compliance, and consistency across the platform.