Senior Data Engineer: Microsoft Fabric & Azure

Flowserve Corporation; Irving (TX); Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Flowserve Corporation is seeking a Senior Data Engineer to lead migration of data products from Azure Synapse to Microsoft Fabric and to design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, SQL, and Python.

The role emphasizes refactoring pandas-based logic to PySpark, handling structured and semi-structured data, and ensuring data quality and performance across Azure platforms. Collaboration with data analysts and engineers is essential.

Formación

• 7–10 years of hands-on data engineering experience.
• Strong background in Microsoft Fabric and Azure fundamentals.
• Experience migrating data products across platforms and building scalable pipelines.
• Proficient in Python and SQL with Pandas experience.
• Familiarity with Spark/PySpark and Spark-based implementations.

Responsabilidades

• Migrate data products from Azure Synapse to Microsoft Fabric.
• Design, build, and support scalable data pipelines and processing workflows.
• Develop data products supporting analytics and business reporting needs.
• Implement Azure Data Factory, notebooks, Spark/PySpark, SQL, and Python solutions.
• Refactor transformation logic for scalable PySpark implementations.
• Collaborate with analysts and stakeholders to gather requirements.
• Document designs, migration approaches, and data product logic.
• Perform data quality checks, performance tuning, and monitoring.

Conocimientos

Microsoft Fabric

Azure Synapse

Azure Data Factory

Spark / PySpark

Python

SQL

Pandas

Git

Azure DevOps

Data modeling

Herramientas

Delta

Parquet

JSON/CSV

CI/CD tooling

Descripción del empleo

Flowserve Corporation is seeking a Senior Data Engineer to lead migration of data products from Azure Synapse to Microsoft Fabric and to design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, SQL, and Python.

The role emphasizes refactoring pandas-based logic to PySpark, handling structured and semi-structured data, and ensuring data quality and performance across Azure platforms. Collaboration with data analysts and engineers is essential.