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Metro location, popular mid-level data role, broad skillset and mid experience amplify competition.
Core data engineering skills are highly transferable across industries despite manufacturing context.
Explicit 5+ years, mandatory PySpark/Azure skills and preferred premier-institute background enforce strict filters.
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Develop and optimize scalable real-time and batch ETL pipelines using Azure Databricks, PySpark, and Apache Spark for manufacturing analytics.
Design and maintain cloud-based data architectures on Azure (preferred), AWS, or GCP implementing Medallion Architecture.
Automate, monitor, and troubleshoot data workflows ensuring high availability, security, and compliance, using CI/CD, DevOps, Docker, and Kubernetes.
5+ years of experience in data engineering with strong expertise in Azure Databricks, PySpark, and Apache Spark.
Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field; preference for IITs, IIITs, NITs, or BITS graduates.
Experience in deploying and optimizing data solutions on cloud platforms (Azure preferred).
2 years of team handling experience.
Experienced in building complex ETL pipelines and cloud data architectures for industrial or manufacturing data.
Has deep expertise with Azure ecosystem and distributed computing frameworks like Spark and Databricks.
Proficient in containerization (Docker, Kubernetes) and automating data workflows with CI/CD and DevOps practices.