Data Engineer

Overview

Design, build and scale enterprise data platforms and cloud-native pipelines that power analytics and decision-making within the Business Intelligence and Analytics organization.

What you'll do

• Design, build and maintain scalable, reliable ELT/ETL pipelines across multiple data sources
• Develop data ingestion frameworks for batch and near real-time data processing
• Ensure data integrity through validation, monitoring and error handling mechanisms
• Optimize pipelines for performance, scalability and cost efficiency
• Design and implement logical and physical data models for analytics and reporting
• Build and maintain data warehouses, data marts and Lakehouse structures
• Apply best practices in schema design (e.g., star/snowflake models)
• Support enterprise data architecture initiatives and standards
• Develop and optimize solutions using Azure Synapse, Azure Databricks, SQL Server and related cloud services
• Support and enhance data platform scalability, reliability and performance
• Leverage distributed processing frameworks (e.g., Spark) for large-scale data transformation
• Implement data quality checks, monitoring and alerting for critical data assets
• Ensure alignment with enterprise data governance, metadata and security standards
• Maintain data lineage and support auditability and compliance requirements
• Uphold data accuracy, consistency and availability SLAs
• Deliver curated, modeled datasets to support BI tools such as Power BI
• Partner with analytics teams to enable dashboards, reporting and self-service analytics
• Tune SQL queries, data models and pipelines for optimal performance
• Identify and resolve performance bottlenecks across data systems
• Recommend improvements to data infrastructure and processing workflows
• Collaborate with business areas and BSA teams to align data requirements
• Translate business needs into scalable and maintainable data solutions
• Support project planning, requirements gathering and solution design
• Follow best practices for version control, testing and deployment of data pipelines
• Contribute to CI/CD processes for data engineering workflows
• Document data flows, models and processes for maintainability and knowledge sharing
• Support integration of AI/ML solutions by enabling reliable data flows between enterprise data platforms and downstream applications
• Support data requirements for model explainability, lineage and governance

What you'll need

• 2-4+ years of experience in data engineering, BI/analytics engineering or data architecture
• Strong proficiency in SQL, data modeling and performance tuning
• Experience building and maintaining ETL/ELT pipelines
• Hands-on experience with cloud data platforms (Azure preferred, including Synapse and Databricks)
• Knowledge of data warehousing, data modeling and database design
• Experience with distributed data processing (e.g., Spark)
• Experience with Power BI or similar BI tools
• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science or a related field

Nice to have

• Master's degree in a relevant discipline
• Microsoft Azure Data Engineer Associate or similar cloud certification
• Databricks or Spark-related certifications
• Relevant certifications in data engineering, cloud platforms or analytics
• Awareness of emerging AI/GenAI capabilities and their data requirements
• Familiarity of integration patterns for Model Context Protocol (MCP) or similar tool-based interfaces
• Exposure to tools such as Python (Pandas, NumPy), MLflow or similar
• Experience working with large-scale datasets for predictive or AI-driven applications

Details

• Location: Bangalore, India