Job Description

Key Responsibilities
Design, architect, and implement scalable data solutions using Azure and the Databricks ecosystem.
Lead the end-to-end lifecycle of data platform implementations-from requirements gathering and design to deployment.
Collaborate with data scientists, developers, and business stakeholders to deliver enterprise-grade solutions.
Stay ahead of emerging technologies to drive innovation and solve complex business challenges.
Mentor and guide data engineering teams, promoting technical excellence and best practices.
Contribute to organizational initiatives such as capability building, solution development, and talent development. Required Qualifications
8+ years of technical experience, with at least 4 years hands-on in Microsoft Azure and Databricks.
Led 2+ end-to-end Data Lakehouse implementations using Azure Databricks and Medallion Architecture.
Expertise in Databricks ecosystem: PySpark, Notebooks, Unity Catalog, Delta Live Tables, Workflows, SQL Warehouse, Mosaic AI, AI/BI Genie.
Strong experience with Azure data tools: ADF, ADLS Gen2, SQL DB, Microsoft Fabric, Event Hub, Stream Analytics, Cosmos DB, Purview, Log Analytics.
Proven ability in building metadata-driven frameworks for data engineering.
Proficient in Python and SQL with strong debugging and optimization skills.
Solid understanding of data modeling (Dimensional and 3NF).
Exposure to LLM/GenAI-powered applications and CI/CD pipelines using Git, Jenkins, or Azure DevOps.
Bonus: Familiarity with Cloudera/Hortonworks, Neo4j, Elasticsearch, or vector databases.
Additional advantage: Knowledge of Azure infrastructure, networking, and security. Educational Qualification
Bachelor's degree (B.E/B.Tech) in Computer Science, Information Technology, or a related discipline from a reputed institute.

Skills Required