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Tier-1 brand, mid-level generalist data role, and in-demand Azure/Databricks skills increase competition.
Core data engineering skills are transferable but Azure/Databricks specialization increases industry and platform specificity.
Explicit 3–8 years requirement plus mandatory Azure/Databricks/ADF/Spark and CI/CD skills makes filters stringent.
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Lead design and implementation of Azure cloud-based data engineering solutions including data pipelines, integration, and transformation using tools like Azure Data Factory and Databricks.
Engage with clients to translate business requirements into technical specifications and develop reusable frameworks and best practices for scalable data warehousing and ETL.
Operate within Agile/DevOps environments to deliver production-ready data solutions ensuring high data quality, security models, and integration with other platforms.
3-8 years of experience working as a data engineer with proficiency in Azure Data Factory, Azure Databricks, and Apache Spark (Python and/or Scala).
Bachelor's or Master's degree in Engineering or equivalent (BE, B.Tech, MCA, M.Tech).
Strong expertise with Azure Data Services including Azure Storage, Azure SQL Data Warehouse, Azure Data Lake, Azure Synapse, Azure Cosmos DB, and Azure Stream Analytics.
Experience with DevOps processes (CI/CD) and Infrastructure as Code; working knowledge of data warehousing principles (Kimball) and security model development.
Experienced in designing and delivering complex cloud-based analytics solutions on Microsoft Azure with deep technical leadership in data engineering practices.
Comfortable operating in Agile delivery models and collaboration with multiple stakeholders to meet business needs and implement scalable data architectures.
Technical proficiency in building performant data pipelines from diverse sources including API and streaming ingestion with a strong focus on solution reusability and quality.