Lead Data Engineer (Databricks, Python)

Roles & 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

Skills: pipelines,azure,spark,apache spark,business requirements,apache,enterprise