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Medium: Pune metro and high demand balanced by specialized Databricks/PySpark requirements.
Medium because Databricks and Azure skills are transferable but require specific platform experience.
High due to mandatory Databricks/PySpark/ADF/Azure expertise and senior technical lead expectations.
Architect, develop, and optimize enterprise-grade data platforms and data pipelines on Azure using Databricks, PySpark, ADF, and SQL.
Build and maintain scalable ELT/ETL pipelines and ingestion frameworks handling structured, semi-structured, and unstructured data from diverse sources.
Implement data quality, schema validation, business logic, and governance controls aligned with Unity Catalog standards, collaborating with stakeholders to ensure reliable production-ready solutions.
Strong hands-on expertise in Databricks, PySpark, Azure Data Factory (ADF), and SQL.
Proven experience building and optimizing scalable ELT/ETL data pipelines with modern data engineering frameworks.
Experience with Delta Lake capabilities (ACID transactions, schema enforcement, time travel) and data governance including Unity Catalog.
Work Experience Required: Not explicitly mentioned in the JD. Location: Pune, India required as per job location.
Experienced in architecting and operationalizing large-scale enterprise data platforms on Azure leveraging Databricks and related cloud services.
Comfortable working closely with IT, business teams, and cross-functional stakeholders to translate requirements into effective data engineering solutions.
Strong technical leader capable of guiding junior engineers and managing complex data workflows and quality assurance frameworks.