Senior Azure Data Engineer for Lakehouse & Databricks

Salt; Brussel; Sur place; EUR 85 000 - 110 000; Plein temps

Résumé du poste

Salt is seeking an experienced Senior Data Engineer to join its high-performing engineering team building a modern, enterprise-scale Azure data platform. You will design, develop and optimise scalable data pipelines powering analytics, AI and business-critical applications.

In an Agile environment, you will work with Azure Databricks, Delta Lake and Lakehouse architectures, building robust ETL/ELT pipelines and tuning Spark workloads for performance and cost efficiency.

Qualifications

• 5+ years of Data Engineering experience.
• Proficient in Python, PySpark and SQL.
• Experience with Lakehouse architecture and Spark tuning.
• Version control with Git.

Responsabilités

• Design, develop and maintain scalable data solutions on Azure.
• Build ETL/ELT pipelines using Python, PySpark, SQL and Spark.
• Develop batch and streaming processing with Delta Lake.
• Design and optimise high-performance Spark workloads for scalability, reliability and cost efficiency.
• Develop reusable data delivery frameworks and components.
• Build CI/CD pipelines using Azure DevOps.
• Implement data quality controls, monitoring and logging.
• Collaborate with Architecture, Cloud, DevOps and AI teams to deliver enterprise-grade data solutions.
• Mentor junior engineers and contribute to engineering standards and code quality.

Connaissances

Data engineering

Python

PySpark

SQL

Lakehouse architecture

Performance tuning

Data modelling

Git

Outils

Azure Databricks

Delta Lake

Azure DevOps

Terraform

Description du poste

Salt is seeking an experienced Senior Data Engineer to join its high-performing engineering team building a modern, enterprise-scale Azure data platform. You will design, develop and optimise scalable data pipelines powering analytics, AI and business-critical applications.

In an Agile environment, you will work with Azure Databricks, Delta Lake and Lakehouse architectures, building robust ETL/ELT pipelines and tuning Spark workloads for performance and cost efficiency.