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Tier-1 brand, popular mid-level data role, and metro location increase applicant competition.
Core data engineering skills transfer across industries, though Azure/Databricks focus raises specialization moderately.
Explicit 3-8 years plus mandatory Azure/Databricks/Spark and ADF skills make shortlisting strict.
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Design, build, and maintain highly performant data pipelines using Azure Data Factory, Apache Spark (preferably Databricks), and API/streaming ingestion.
Provide technical and thought leadership on Azure data & analytics services encompassing data integration, processing, modeling, and visualization to enable cloud-based data warehousing and ETL solutions.
Collaborate with clients to translate business requirements into technical specifications and deliver solutions in an Agile/DevOps environment ensuring data quality and consistency.
3-8 years of relevant work experience in data engineering with expertise in Azure and Apache Spark technologies.
Bachelor's or Master's degree in Engineering (BE, B.Tech, M.Tech) or MCA.
Proficient with Azure Data Factory, Azure Synapse, Azure SQL, Azure Data Lake, Azure Databricks, and related cloud-based analytics services.
Experience with DevOps processes including CI/CD and Infrastructure as Code; knowledge of data warehousing concepts and modeling (Kimball methodology).
Hands-on experience delivering end-to-end Azure cloud data solutions, including data pipeline design, implementation, and integration within enterprise projects.
Able to lead technically in cloud data engineering practices, driving reuse and standardization of code and frameworks for scalable and quality data workflows.
Comfortable working in Agile/DevOps environments collaborating with stakeholders to align technical efforts with business needs.