Assistant Manager

Role: Data Bricks Developer Experience: 5+ Years Location: Gurgaon OR Bangalore Work Mode: Work From Office [5 Days Office] POSITION SUMMARY The Databricks Data Engineer will be responsible for designing, building, and optimizing scalable data pipelines and lakehouse solutions using Databricks. The role requires strong hands-on experience in data engineering, distributed data processing. ROLES AND RESPONSIBILITIES: • Design, build, and maintain ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake. • Develop and optimize data ingestion frameworks, data transformations, and end to end workflows for batch and streaming use cases. • Implement Delta Lake based architectures, including versioning, schema evolution, and ACID compliant pipelines. • Work with stakeholders to understand data requirements and translate them into scalable data engineering solutions. • Manage and optimize Databricks clusters, jobs, and notebooks for performance and cost efficiency. • Ensure data quality, reliability, and observability through validation frameworks and monitoring. • Contribute to data modeling, metadata management, and best practices within the data platform. REQUIRED QUALIFICATIONS • 3+ years of experience in data engineering with 2+ years of hands on expertise in Databricks. • Hands on experience with Spark (PySpark/Spark SQL) and distributed data processing. • Solid SQL knowledge and experience working with large-scale datasets • Strong understanding of Delta Lake, medallion architecture, and scalable lakehouse patterns. • Good understanding of CI/CD, Git, and modern DevOps practices for data pipelines. • Familiarity with structured/unstructured data, data quality frameworks, and performance tuning. EDUCATION: Bachelor’s degree in computer science, Software Engineering, MIS or equivalent combination of education and experience KEY SKILLS: Data Engineering, Python, Pyspark, Azure Cloud, Azure Data Bricks • Design, build, and maintain ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake. • Develop and optimize data ingestion frameworks, data transformations, and end to end workflows for batch and streaming use cases. • Implement Delta Lake based architectures, including versioning, schema evolution, and ACID compliant pipelines. • Work with stakeholders to understand data requirements and translate them into scalable data engineering solutions. • Manage and optimize Databricks clusters, jobs, and notebooks for performance and cost efficiency. • Ensure data quality, reliability, and observability through validation frameworks and monitoring. • Contribute to data modeling, metadata management, and best practices within the data platform. REQUIRED QUALIFICATIONS • 3+ years of experience in data engineering with 2+ years of hands on expertise in Databricks. • Hands on experience with Spark (PySpark/Spark SQL) and distributed data processing. • Solid SQL knowledge and experience working with large-scale datasets • Strong understanding of Delta Lake, medallion architecture, and scalable lakehouse patterns. • Good understanding of CI/CD, Git, and modern DevOps practices for data pipelines. • Familiarity with structured/unstructured data, data quality frameworks, and performance tuning. Bachelor’s degree in computer science, Software Engineering, MIS or equivalent combination of education and experience