Description

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.
- Valuable 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

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.

Qualifications

Bachelor’s degree in computer science, Software Engineering, MIS or equivalent combination of education and experience

📌 Assistant Manager (Bengaluru)
🏢 EXL
📍 Bengaluru