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Metro locations and mid-level data engineer demand make applicant competition medium.
Data engineering skills are transferable across industries, though Azure/Databricks specialization moderately reduces portability.
Explicit 6–8 years and specific Azure/Databricks/Kafka tech requirements make shortlisting highly strict.
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Design, develop, and maintain scalable Azure-based data pipelines using technologies such as Azure Databricks, Apache Spark, PySpark, and Kafka for batch and real-time processing.
Build and optimize enterprise-scale data platforms, leveraging Lakehouse architectures, Delta Lake, and cloud-native Azure services while ensuring data quality, integrity, and operational excellence.
Collaborate with cross-functional teams to translate business requirements into scalable technical solutions and support governance, security, and compliance of enterprise data environments.
6 to 8 years of experience in Data Engineering and Cloud Data Platforms.
Strong hands-on expertise in Azure Databricks, Apache Spark, PySpark, and Python; Scala is preferred.
Experience with Azure Data Services including Azure Data Factory, Delta Lake, and Lakehouse architectures.
Experience with production support practices, CI/CD pipelines, DevOps tools like Azure DevOps and Git; Location: Pune, Airoli, Mumbai.
Experienced in designing and optimizing large-scale distributed data processing workloads on Microsoft Azure cloud ecosystems.
Proficient in developing both batch and real-time streaming data solutions, with strong focus on operational monitoring, validation, and error handling frameworks.
Able to work effectively with architects, data scientists, business stakeholders, and engineering teams to deliver scalable and secure cloud data platforms aligned with enterprise needs.