Azure Data Engineer

As a preferred supplier to one of our biggest

Financial

Client , I am seeking for

Azure Data Engineer

for a position in

Amsterdam, Netherlands . As a Senior Data Engineer within the Transaction Banking Domain Data Store (TB DDS) team, you will help build and evolve our cloud-based data platform. You will design, develop, and operate scalable data solutions that enable data-driven decision making and support critical Transaction Banking processes.

Key Responsibilities:

Design, develop, and maintain scalable data pipelines and data products using Azure Databricks, PySpark, Python, and SQL.

Build and optimize data ingestion, transformation, and storage solutions for analytics, reporting, and operational use cases.

Develop and support ETL/ELT processes that integrate data from multiple sources into trusted and governed data platforms.

Design and review cloud-native solutions leveraging Azure Data Lake Storage (ADLS), Azure Functions, Databricks, and Apache Airflow.

Ensure data quality, reliability, scalability, monitoring, and operational excellence across the data ecosystem.

Implement and maintain source control, CI/CD pipelines, and deployment automation using Azure DevOps.

Collaborate closely with business and technical stakeholders to translate requirements into robust and sustainable solutions.

Enhance existing data products, optimize platform capabilities, and continuously improve engineering practices.

Troubleshoot and resolve complex production issues using strong analytical and problem-solving skills.

Contribute to data architecture, engineering standards, and best practices across the organization.

Promote innovation, knowledge sharing, and continuous improvement within the Data Engineering Chapter.

Familiar with Agile, DevOps, and DataOps principles as part of a cross-functional Scrum team.

Hybrid working with 2 days in office

Requirement

Strong hands-on experience with Azure Databricks [including Unity catalog, DAB], Apache Airflow, ADLS, and Azure Functions.

Proficiency in Python (OOP), PySpark, and SQL.

Solid understanding of Apache Spark performance tuning and large-scale data processing.

Experience building and maintaining ETL/ELT pipelines and modern data integration solutions.

Experience with Git, CI/CD practices and Azure DevOps.

Knowledge of data modelling techniques, including dimensional modelling.

Strong analytical, troubleshooting, and communication skills.

Experience working in Agile/Scrum and DevOps environments.

Nice to Have:

Experience with monitoring, observability, logging, and data lineage.

Familiarity with Delta Lake, Iceberg, or other modern data lake technologies.

Knowledge of data governance, metadata management, and data quality frameworks.

Azure and/or Databricks certifications.

Experience in Financial Services or Transaction Banking

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