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Key skills for this role
About the Role
Cognizant is seeking a Senior Data Engineer / Tech Lead to design, develop, and optimize scalable cloud-native data platforms on Azure. The role involves building ETL/ELT pipelines, implementing Medallion architecture, and providing technical mentorship.
Key Skills for This Role
Responsibilities
- Design, build, and maintain complex, scalable ETL/ELT pipelines using Azure Data Factory to ingest heterogeneous data from diverse sources
- Implement parameter driven dynamic pipelines, custom activities, and robust scheduling triggers
- Configure and manage Integration Runtimes for secure and efficient on premise to cloud data movement
- Develop high performance batch and real time streaming data pipelines using Azure Databricks, PySpark, Spark SQL, or Scala
- Implement Medallion Architecture (Bronze, Silver, and Gold layers) and maintain modern Lakehouse architectures utilizing Delta Lake
- Optimize Spark compute usage through parameter driven jobs, proper cluster scaling, and Databricks serverless compute configurations
- Perform deep performance tuning, query optimization, and memory management of heavy Spark workloads
- Design and refine data models including dimensional modeling, star schemas, and Slowly Changing Dimensions
- Optimize databases by writing complex, performant T SQL queries, stored procedures, and schemas on Azure SQL Database or Azure Synapse Analytics
- Enforce data governance, lineage, and access controls across the platform utilizing Unity Catalog
- Secure data architectures utilizing Azure Key Vault, Managed Identities, Service Principals, and VNET integrated environments
- Implement CI/CD pipelines using Git and Azure DevOps to automate database and pipeline deployments
Requirements
- Minimum of 8+ years of professional experience in Data Engineering or Data Warehousing
- 4+ years of dedicated, hands on experience designing and operating modern data platforms on Microsoft Azure
- Proficient in Python/PySpark, SQL (advanced query optimization, indexing, stored procedures)
- Extensive knowledge of Azure Data Factory, ADLS Gen2, Azure Databricks (Delta Lake), Azure SQL Database, and Azure Synapse Analytics
- Robust understanding of Apache Spark architecture, distributed computing, and the Hadoop ecosystem
- Strong background in CDC mechanisms, API integrations, and event driven data streaming (e.g., Event Hubs/Kafka)
- Excellent analytical capabilities, strong documentation skills, and ability to articulate technical strategies to non technical business partners
Full Job Posting
Role Overview
- As a Senior Data Engineer / Tech Lead, you will design, develop, and optimize scalable, enterprise grade cloud native data platforms and pipelines on the Microsoft Azure ecosystem.
- You will possess deep technical ownership and be responsible for implementing modern data architectures (such as Lakehouse and Medallion architecture).
- This role requires close collaboration with architects, business analysts, and downstream data consumers to translate complex business requirements into high performing and secure data solutions.
Key Responsibilities
- Design, build, and maintain complex, scalable ETL/ELT pipelines using Azure Data Factory (ADF) to ingest heterogeneous data from diverse sources (APIs, SFTP, Oracle Fusion, SQL Server, Event Hubs, etc.)
- Implement parameter driven dynamic pipelines, custom activities, and robust scheduling triggers
- Configure and manage Integration Runtimes (Self Hosted and Azure IR) for secure and efficient on premise to cloud data movement
- Develop high performance batch and real time streaming data pipelines using Azure Databricks, PySpark, Spark SQL, or Scala
- Implement Medallion Architecture (Bronze, Silver, and Gold layers) and maintain modern Lakehouse architectures utilizing Delta Lake
- Optimize Spark compute usage through parameter driven jobs, proper cluster scaling, and Databricks serverless compute configurations
- Perform deep performance tuning, query optimization, and memory management of heavy Spark workloads
- Design and refine data models including dimensional modeling, star schemas, and Slowly Changing Dimensions (SCDs)
- Optimize databases by writing complex, performant T SQL queries, stored procedures, and schemas on Azure SQL Database or Azure Synapse Analytics
- Enforce data governance, lineage, and access controls across the platform utilizing Unity Catalog
- Secure data architectures utilizing Azure Key Vault, Managed Identities, Service Principals, and VNET integrated environments
- Implement CI/CD pipelines using Git and Azure DevOps to automate database and pipeline deployments
Required Skills & Qualifications
- Overall Experience: Minimum of 8+ years of professional experience in Data Engineering or Data Warehousing
- Azure Experience: 4+ years of dedicated, hands on experience designing and operating modern data platforms on Microsoft Azure
- Programming Languages: Proficient in Python/PySpark, SQL (advanced query optimization, indexing, stored procedures), and optionally Scala
- Core Azure Stack: Extensive knowledge of Azure Data Factory, ADLS Gen2, Azure Databricks (Delta Lake), Azure SQL Database, and Azure Synapse Analytics
- Big Data Concepts: Robust understanding of Apache Spark architecture, distributed computing, and the Hadoop ecosystem
- Data Integration: Strong background in CDC (Change Data Capture) mechanisms, API integrations, and event driven data streaming (e.g., Event Hubs/Kafka)
- Soft Skills: Excellent analytical capabilities, strong documentation skills, and the ability to articulate technical strategies to non technical business partners
Preferred / Good to Have Qualifications
- Certifications: Microsoft Certified: Azure Data Engineer Associate (DP 203), Azure Solutions Architect Expert, or Databricks Certified Associate/Professional
- Cloud Platforms: Exposure to multi cloud environments (e.g., Google Cloud Platform or AWS)
- IaC: Experience using Infrastructure as Code (IaC) tools like Terraform or ARM Templates to deploy Azure resources
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