Azure Data Engineer

Azure Data Engineer – Offshore, India

Experience: 8–10+ years
Engagement: 3 months
Location: Offshore, India

Role Summary:
We are looking for an experienced Azure Data Engineer to design, develop, and optimize data integration and ETL/ELT pipelines using Microsoft Azure technologies. The resource will work with the CIT Data Team to build scalable data solutions and integration feeds across various applications.

Key Responsibilities:

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  Design and develop data pipelines using Azure Data Factory (ADF).

  
  


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  Build and optimize integrations across ADLS, Azure Synapse, Databricks, Azure SQL, SAP, XML, flat files, and databases.

  
  


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  Develop ETL/ELT solutions using SQL, Python/PySpark, Spark, and Azure services.

  
  


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  Perform data modeling, data warehousing, data governance, metadata, and data quality activities.

  
  


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  Optimize pipeline performance and ensure adherence to IFC technical standards.

  
  


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  Develop unit/integration test cases and support application feed testing.

  
  


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  Prepare technical/functional design documents and other project artifacts.

  
  


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  Work in an Agile/SDLC environment and collaborate with project leads and business/data teams.

  
  

Mandatory Skills:

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  Strong hands-on experience with Azure Data Factory, Azure Databricks, ADLS, Azure Synapse, Azure SQL.

  
  


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  Strong SQL Server/Oracle and complex SQL query skills.

  
  


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  Experience with Python/PySpark and Spark.

  
  


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  Strong ETL/ELT and data warehouse/data lake experience.

  
  


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  Experience with Informatica and/or SSIS is an advantage.

  
  


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  Knowledge of Azure Functions, Logic Apps, Automation, PowerShell, AzCopy, Azure DevOps/ARM templates.

  
  


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  Strong understanding of data architecture, modeling, ingestion, curation, and raw/enriched/curated data layers.

  
  


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  Experience delivering end-to-end data warehousing, BI, and analytics projects.

  
  


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  Bachelor’s degree in Computer Science, Engineering, MIS, or related field.

  
  

Preferred: Experience with real-time/near-real-time data warehousing and enterprise data integration.