Role: Sr. Data Engineer
Location: Gurugram & Bangalore
Work Mode: Work from Office [5 Days from office]
Role Overview:
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.
Key Role 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.
• Integrate Databricks pipelines with cloud services (Azure Data Lake / AWS S3, Event Hub/Kafka, Synapse/Redshift, etc.).
• Ensure data quality, reliability, and observability through validation frameworks and monitoring.
• Contribute to data modeling, metadata management, and best practices within the data platform.
• Collaborate closely with data scientists, analysts, and business teams to support analytics and ML workloads.
Must Have:
• 8+ years of experience in data engineering with strong 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: Databricks, Pyspark, SQL
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.
• Integrate Databricks pipelines with cloud services (Azure Data Lake / AWS S3, Event Hub/Kafka, Synapse/Redshift, etc.).
• Ensure data quality, reliability, and observability through validation frameworks and monitoring.
• Contribute to data modeling, metadata management, and best practices within the data platform.
• Collaborate closely with data scientists, analysts, and business teams to support analytics and ML workloads.
Qualifications
• 8+ years of experience in data engineering with strong 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
Skills Required
- 8+ years of experience in data engineering with strong 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 and unstructured data, data quality frameworks, and performance tuning
- Experience integrating Databricks with cloud services (Azure Data Lake, AWS S3, Event Hub, Kafka, Synapse, Redshift)
- Bachelor's degree in Computer Science, Software Engineering, MIS or equivalent
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
