- Lead the design, development, and implementation of scalable data engineering solutions using Databricks, ensuring high-quality and reliable data pipelines for enterprise and semiconductor/manufacturing use cases.

- Provide technical leadership to a team of data engineers, guide day-to-day development activities, conduct code/design reviews, resolve technical challenges, and ensure adherence to engineering best practices.

- Demonstrate strong hands-on expertise in SQL, including complex joins, CTEs, subqueries, window functions, aggregations, data transformation, performance tuning, and troubleshooting of complex queries.

- Strong working knowledge of Databricks, PySpark, Delta Lake, notebooks, workflows/jobs, data transformation, and ETL/ELT concepts.

- Should be comfortable remaining hands-on while leading the team.

- Act as a key technical point of contact for client stakeholders.

- Independently participate in discussions with business and technical teams, understand their requirements, ask the right questions, and provide clear technical perspectives and solutions.

- Work closely with business stakeholders to understand data requirements and translate business problems into technical requirements, data models, transformation logic, and actionable development tasks for the engineering team.

- Excellent verbal and written communication skills with the confidence to lead discussions with senior client stakeholders.

- Should be able to explain complex technical concepts in a simple and business-friendly manner and confidently challenge or clarify requirements when required.

- Experience working with semiconductor, manufacturing, supply chain, equipment, production, quality, or other industrial data is preferred.

- Domain Advantage : Exposure to high-volume, complex, and time-series manufacturing datasets will be an advantage.

- Ensure data quality, reliability, scalability, and performance across pipelines.

- Troubleshoot data issues, identify root causes, and work with relevant teams to implement sustainable solutions.

- Take end-to-end ownership of assigned data engineering initiatives, including requirement clarification, technical design, task allocation, development oversight, stakeholder communication, delivery tracking, and production support.

- Team Management : Should be capable of managing multiple priorities and coordinating a sizeable engineering team.