Senior Data Engineer
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain Databricks ETL/ELT pipelines with PySpark, Spark SQL and Delta Lake
- Create ingestion and transformation workflows for both batch and streaming data
- Apply Delta Lake features such as versioning and schema changes in reliable lakehouse pipelines
- Tune Databricks clusters, jobs and notebooks for speed and cost efficiency
- Connect pipelines to cloud storage and messaging services, while supporting data quality and analytics workloads
What you bring
- At least eight years in data engineering, including substantial hands-on work with Databricks
- Practical Spark experience, including PySpark or Spark SQL, and distributed data processing
- Strong SQL skills and experience handling large datasets
- A bachelor's degree in computer science, software engineering, MIS or a related equivalent background
Who this fits
This senior role suits someone who can turn stakeholder data needs into scalable pipelines and work with data scientists, analysts and business teams. The role is office-based five days a week in Gurugram or Bangalore.
From the employer
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
• 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.
• 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
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