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About the role
Role & Responsibilities Key Responsibilities - Architect and implement enterprise-grade Lakehouse solutions using Databricks - Design and deliver scalable batch and real-time data pipelines using Apache Spark (PySpark/SQL) - Build ETL/ELT pipelines, incremental data loads, and metadata-driven ingestion frameworks - Implement and optimize Databricks components: Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, and Workflows - Design large-scale data warehousing solutions with 3NF and dimensional modeling - Establish data governance, security, and data quality frameworks, including Unity Catalog - Lead ML lifecycle management using MLflow and drive AI use cases (RAG, AI/BI) - Manage cloud-native deployments on Microsoft Azure and integrate with enterprise systems (e.g., ServiceNow) - Drive CI/CD, DevOps practices, and performance optimization of Spark workloads - Provide technical leadership, mentor teams, and ensure successful delivery - Collaborate with stakeholders to translate business requirements into scalable solutions Ideal Candidate - Strong Databricks Architect Profile with end-to-end Lakehouse ownership - Mandatory (Experience 1) Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development. - Mandatory (Experience 2) Must have atleast 5+ years of expertise across the Databricks ecosystem Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog - Mandatory (Tech skill 1) Must have worked at architecture level, owning end-to-end design through deployment - Mandatory (Tech skill 2) Must have robust experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability - Mandatory (Tech skill 3) Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems - Manda .