Assistant Manager
About the role
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