Responsibilities :
Key Responsibilities: Solution Delivery & Leadership
Lead end-to-end implementation of data engineering solutions on Azure Databricks, ensuring scalability, reliability, and maintainability.
Drive technical design discussions and translate business requirements into well-structured Databricks/Genie-based solutions.
Provide technical guidance, code reviews, and mentorship to ensure consistent engineering standards across the team. Databricks & Genie Development
Build and optimize notebooks, jobs, and workflows leveraging Azure Databricks and Genie capabilities.
Develop reusable components and patterns to accelerate delivery across multiple use cases.
Troubleshoot production issues, perform root-cause analysis, and implement preventive improvements. Performance, Quality & Operations
Optimize cluster configurations, job performance, and resource usage to balance speed and cost.
Establish monitoring and operational practices for pipeline health, failures, and SLAs.
Ensure data quality checks and validation steps are embedded into pipelines and workflows. Minimum Qualifications:
5â€8 years of overall experience in data engineering / analytics engineering roles with ownership of production-grade delivery.
Strong hands-on experience with Azure Databricks and Genie for building and managing data workflows.
Solid experience with Databricks development and operationalization (jobs, workflows, notebooks).
Ability to lead technical discussions, perform reviews, and guide implementation best practices.
Education: BTECH, MTECH, MCA, MSC.
Additional Responsibilities:
Preferred Qualifications:
Experience designing scalable data processing patterns and reusable frameworks within Databricks environments.
Proven track record of performance tuning and cost optimization for Databricks workloads in enterprise settings.
Experience establishing engineering standards (branching, reviews, release practices) and mentoring team members.
Strong stakeholder management skills with the ability to align technical delivery to business outcomes.
Exposure to production support models, incident handling, and continuous improvement for data platforms.