NPS Prism

Data Engineer

• Design and own scalable data architectures for ingestion, transformation, and analytics on Databricks
• Build robust ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows
• Lead performance tuning, partitioning, and data optimization across large distributed systems
• Mentor junior data engineers and enforce best practices for code quality, testing, and version control
• Develop and maintain data lakes and data warehouses on cloud platforms (Azure Data Lake, AWS S3, GCP BigQuery, etc.)
• Utilize Azure Data Factory, AWS Glue, or similar orchestration tools to manage large-scale data workflows
• Enable seamless model training and inference pipelines on Databricks
• Define and implement frameworks for data validation, monitoring, and error handling
• Automate testing, deployment, and monitoring for data workflows to ensure reliability and repeatability
• Collaborate with product and business teams to translate analytical requirements into scalable technical designs

Staff Data Engineer

• Design, build, and optimize ETL/ELT workflows using tools like Databricks, SQL, Python/pyspark & Alteryx (Good to have)
• Develop and maintain robust, scalable, and efficient data pipelines for processing large datasets
• Work on cloud platforms (Azure, AWS) to build and manage data lakes, data warehouses, and scalable data architectures
• Utilize cloud services like Azure Data Factory, AWS Glue, or similar for data processing and orchestration
• Use Databricks for big data processing, analytics, and real-time data processing
• Create and manage SQL-based data solutions, ensuring high availability, scalability, and performance
• Collaborate with cross-functional teams to deliver impactful data solutions
• Leverage CI/CD pipelines to streamline development, testing, and deployment of data engineering workflows
• Maintain clear documentation for data workflows, pipelines, and processes

• Design and own scalable data architectures for ingestion, transformation, and analytics on Databricks
• Build robust ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows
• Lead performance tuning, partitioning, and data optimization across large distributed systems
• Mentor junior data engineers and enforce best practices for code quality, testing, and version control
• Develop and maintain data lakes and data warehouses on cloud platforms
• Utilize Azure Data Factory, AWS Glue, or similar orchestration tools to manage large-scale data workflows
• Ensure compliance with Bain’s data security and privacy standards
• Implement CI/CD pipelines for data code deployments using Git, Azure DevOps, or Jenkins.