Data Engineer

Knowledge and Skills
Core Engineering Languages: Expert proficiency in SQL and Python (PySpark); strong working knowledge of Java for backend data processing or SDK integrations.
Generic Data Engineering: Solid understanding of Software Engineering principles applied to data (DRY code, modularity, error handling, and logging).
Data Structures: Proficiency in working with structured, semi‐structured (JSON/XML), and unstructured data formats.
DevOps & CI/CD: Practical experience with Git, branching strategies, and automating deployments through Azure DevOps or GitHub Actions.
Distributed Computing: Strong grasp of Apache Spark internals, memory management, and execution plans.
Languages: Good knowledge of written and spoken English (working language). Knowledge of French is an asset.
Specific Expertise
Fabric Delta Lake Management: Expertise in optimizing and managing transaction logs within the Fabric Lakehouse.
Multi‐stack Integration: Proven ability to connect Fabric with non‐Microsoft tools, utilizing generic JDBC/ODBC drivers or REST APIs.
Performance Tuning: Deep experience in identifying bottlenecks in data flows and optimizing code for low‐latency processing.
Data Design Patterns: Practical experience implementing standard patterns such as SCD (Slowly Changing Dimensions), Change Data Capture ingestion, and idempotency in a Delta Lake environment.
Certifications & Standards
Microsoft Focus: DP-700 (Microsoft Certified: Fabric Data Engineer Associate) – required.
Generic/Cloud Focus: DP-203 (Azure Data Engineer) or equivalent certifications from Databricks (Data Engineer Associate) or AWS – a plus.
Benefits
We offer 100% remote opportunities and a professional, dynamic, and collaborative team environment.
International career opportunities across Portugal, Belgium, Luxembourg, Denmark, Netherlands, Singapore, and the United States.
Health and Life Insurance.
Tech Visa support for non‐EU applicants.