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