Must have

• Microsoft Fabric

• Power BI

• SQL

• PySpark

• Azure

• CI/CD

• English (B2)

Requirements description

Bachelor s/Master s degree in Computer Science, Information Systems, or related field.Strong hands-on experience with Microsoft Fabric components:

Data Factory

Lakehouse / OneLake

Synapse Data Engineering

Power BI

Experience with data modeling (star/snowflake) and performance tuning in Power BI.

Deep understanding of modern data architecture patterns including lakehouse, medallion architecture, and ELT frameworks.

Expertise in SQL, PySpark, T-SQL, DAX, and Power Query (M language).

Experience modernizing platforms from SSIS, Informatica, or Hadoop to cloud-native tools.

Familiarity with Azure ecosystem Azure Data Lake, Azure SQL DB, Azure Functions, Azure Synapse, Azure Data Factory.

Strong experience in CI/CD pipelines, preferably with Azure DevOps.

Familiarity with data security, GDPR, HIPAA, and enterprise data governance.

Soft Skills:

Strong analytical and problem-solving skills.

Excellent communication and stakeholder management capabilities.

Ability to lead projects, mentor junior engineers, and collaborate with cross-functional teams

Offer description

We are seeking an experienced Senior Data Engineer with strong expertise in Microsoft Fabric to support our enterprise data modernization and analytics transformation initiatives. The ideal candidate will have a deep understanding of data pipelines, lakehouse architecture, Power BI, Synapse integration, and experience in modernizing legacy data systems to cloud-native solutions. This role is critical in building scalable, secure, and high-performing data solutions on the Microsoft ecosystem.

Your responsibilities

1. Design and implement scalable data pipelines using Microsoft Fabric components such as Data Factory, Synapse Data Engineering, and OneLake.
2. Build and maintain lakehouse architectures using Delta Lake, Parquet, and OneLake within the Microsoft Fabric ecosystem.
3. Modernize legacy ETL/ELT workflows by migrating from tools like SSIS, Informatica, or Hadoop to cloud-native Azure solutions.
4. Collaborate with Data Architects, BI Developers, and Analysts to deliver scalable and maintainable data models for reporting and analytics.
5. Optimize Power BI datasets and reports, ensuring performance, efficiency, and usability through effective data modeling and DAX.
6. Implement data governance and security practices, including data lineage, role-based access controls (RBAC), and tools such as Microsoft Purview.

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