Databricks Developer_ AMAT

Exp: 5+ Yrs
Location: ITPL, Bangalore
Work Mode: 4days Work from Office
Customer: AMAT
Billrate:22-24 LPA

Mandates:
Looking for candidates in Bangalore location only and mandatory work from office (AMAT Client Office location)
Mandatory to attend the final round of the interview in person.

JD:

" Design, build, and optimize data pipelines and ETL/ELT workflows using Databricks and Apache Spark (PySpark). " Develop scalable, high performance data solutions using Spark distributed processing. " Lead engineering initiatives focused on automation, performance tuning, and platform modernization. " Implement and manage CI/CD pipelines using Git-based workflows and tools such as GitHub Actions or Jenkins. " Collaborate with cross-functional teams to translate business needs into technical solutions. " Ensure data quality, governance, and security across all processes. " Troubleshoot and optimize Spark jobs, Databricks clusters, and workflows. " Participate in code reviews and develop reusable engineering frameworks. " Should have knowledge of utilizing AI tools to improve productivity and support daily engineering activities. " Strong knowledge and hands-on experience in Databricks Genie, including prompt engineering, workspace usage, and automation. Required Skills & Experience: " 5+ years of experience in Data Engineering or related fields. " Strong hands-on expertise in Databricks (notebooks, Delta Lake, job orchestration). " Deep knowledge of Apache Spark (PySpark, Spark SQL, optimization techniques). " Strong proficiency in Python for data processing, automation, and framework development. " Strong proficiency in SQL, including complex queries, performance tuning, and analytical functions. " Strong knowledge of Databricks Genie and leveraging it for engineering workflows. " Strong experience with CI/CD and Git-based development workflows. " Proficiency in data modeling and ETL/ELT pipeline design. " Experience with automation frameworks and scheduling tools. " Solid understanding of distributed systems and big data concepts.