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Niche Databricks/Azure senior role reduces candidate density despite Gurgaon metro location.
Core Databricks and Azure data engineering skills are transferable, though insurance domain knowledge is advantageous.
Explicit 8–12 years plus mandatory Databricks, Azure, PySpark and Delta Lake skills make filters highly strict.
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Lead architectural design and development of enterprise-scale data ecosystems on Azure using Databricks and Delta Lake.
Build and optimize high-volume ETL/ELT pipelines with PySpark, Databricks workflows, enforcing best practices, data quality, and validation frameworks.
Ensure end-to-end data pipeline reliability, performance tuning, compliance, and governance supporting analytics and business-critical reporting.
8-12 years of experience in Data Engineering with Databricks and Azure Cloud technologies.
Bachelor’s degree in Computer Science, IT, or related field.
Strong proficiency in PySpark, Python, SQL, plus experience with Azure Data Factory, Azure Databricks, Azure Data Lake and Delta Lake.
Experience implementing data quality frameworks, performance tuning Spark jobs, and enforcing governance with Unity Catalog or similar tools.
Experienced in designing and managing scalable data architectures aligned to enterprise analytics and ML use cases, preferably in insurance domain.
Hands-on expertise deploying end-to-end data pipelines with automation, CI/CD, and cloud-native Azure ecosystem integration.
Able to influence data engineering strategy with deep technical mastery of Databricks features and governance policies to balance cost and performance.