I build production-oriented data pipelines that don't break silently. My focus is on quality, observability, and deterministic execution.

Validate early → Fail loudly → Monitor continuously

• 🧊 Data quality enforcement using robust schema validation rules
• 🔭 Deterministic and reproducible execution across all environments
• 🧪 Clear observability and failure signals to eliminate silent pipeline drops
• 📂 Testable and maintainable data workflows built for long-term scalability

A production-style DataOps pipeline designed to prevent silent data corruption and stale data issues.

Core & Validation:

Data & Config:

Database:

Infrastructure & Testing:

DevOps Basics & Monitoring:

What it demonstrates:

• 🏗️ Medallion Architecture (Bronze → Silver → Gold)
• 📐 Schema validation using Pydantic
• 🔄 Idempotent pipeline execution
• 🧹 Data cleaning & transformation with Pandas
• 🗄️ SQL-based storage & querying
• 🧪 Automated testing with Pytest
• 🐳 Containerized execution with Docker
• 👷 CI pipeline via GitHub Actions
• 🧱 Orchestration with apache-Airflow

I focus on building systems that are predictable, debuggable, and production-ready.

• ✅ Validate data before processing
• ⚠️ Handle edge cases and invalid inputs
• ✍️ Write tests for transformations
• 🔄 Keep pipelines deterministic
• 📢 Surface failures clearly

Actively seeking a remote EU/UK DataOps or Data Engineering role — contract or full-time.

I build reliability into pipelines from the ground up: schema validation, idempotency, and observability baked in rather than bolted on afterward.

• 🎯 Role Focus: Data Engineer / DataOps Engineer — Open to remote & on-site (relocation with sponsorship), full-time or contract
• 🕒 Availability: Full-time, overlapping with CET/CEST business hours
• 🧩 What I bring: Production-minded pipeline design, strong test coverage, fast onboarding
• 🛡️ Approach: Fail loudly, validate early, keep systems debuggable under pressure