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oracle

Data Engineer

Zensar
IND
Senior · 6+ years experience
Onsite
Discovered 1 weeks ago
azureazure-data-factorydatabricksdelta-lakepower-bipython

Full Job Posting

Qualifications

  • 6+ years of hands‐on data engineering experience in SQL‐heavy or enterprise data environments.
  • Deep expertise in Microsoft SQL Server and advanced T‐SQL development, including query optimization.
  • Practical experience with Microsoft Fabric, Lakehouse architecture, and Delta Lake.
  • Proficiency in Spark (PySpark preferred) and Python notebooks for distributed data transformation.
  • Experience with Azure Data Factory, Databricks, Azure Analysis Services, ADLS Gen2, and Purview.
  • Strong knowledge of dimensional modeling (Kimball) and semantic model design for Power BI.
  • Proven ability to implement incremental loading and CDC patterns in hybrid environments.
  • Familiarity with CI/CD pipelines, testing frameworks, monitoring, and data governance principles.
  • Background in financial services, banking, or capital markets.
  • Microsoft certifications (Azure Data Engineer, Fabric) and excellent facilitation, presentation, and influencing skills.

Responsibilities

  • Build and maintain bronze, silver, and gold lakehouse layers in Microsoft Fabric and Databricks.
  • Develop ingestion and transformation pipelines using notebooks and PySpark.
  • Implement incremental loading and change data capture (CDC) from on‐prem SQL to Fabric/Databricks.
  • Optimize Delta tables for performance and cost efficiency.
  • Create and sustain Azure Data Factory pipelines and Azure Analysis Services cubes.
  • Own data products as managed assets: version control, testing, monitoring, and continuous improvement.
  • Ensure datasets comply with defined data contracts, governance, and platform standards.
  • Collaborate with business stakeholders, analysts, and the central Data Platform team.
  • Design and optimize semantic models for enterprise analytics platforms such as Power BI.
  • Mentor junior engineers and champion architectural standards and modern technologies.

Preferred Qualifications

  • Proficiency in Spark (PySpark preferred) and Python notebooks for distributed data transformation.

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