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Strong employer brand, metro location, mid-level generalist data role, and broad skillset make competition high.
Technical data engineering skills are fairly transferable across industries but some consultancy-specific governance reduces fit flexibility.
Explicit 6+ years plus many mandatory Azure/Databricks/Spark skills creates a high shortlisting barrier.
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Design, build, and maintain scalable ETL/ELT pipelines on Azure using Apache Spark (PySpark/Scala) in Databricks/HDInsight environments.
Orchestrate complex data workflows with Azure Data Factory; implement and manage data lakes and curated data layers (raw, staged, curated) with appropriate schema, partitioning, and lifecycle policies.
Optimize Spark and cluster performance; operationalize production data pipelines with monitoring, alerting, security, and governance best practices including CI/CD deployments.
5-7 years of experience as a Data Engineer or similar with a strong focus on Azure data services.
Expertise in Apache Spark (PySpark and/or Scala), Databricks, Azure HDInsight, Azure Data Factory, and Python with strong SQL skills.
Bachelor of Engineering (BE/B.Tech) and/or MBA degree required; MCA acceptable.
Experience with Azure Blob Storage/ADLS Gen2, Azure Key Vault, Azure Monitor/Log Analytics, and Hadoop ecosystem fundamentals.
Experienced in building production-grade, large-scale data pipelines on Azure with strong focus on data modeling, performance tuning, and operational excellence.
Familiar with modern data platform technologies including Lakehouse (Delta Lake, Unity Catalog), infrastructure as code, CI/CD pipelines, and data governance tools.
Able to collaborate cross-functionally with Analytics, Data Science, and Product teams and manage secure, compliant data environments with a strong operational mindset.