Data Engineer 2

Role Summary

Key Responsibilities

• Design, develop, test, and maintain scalable ETL/ELT pipelines for enterprise processing and analytics.
• Perform data ingestion, extraction, transformation, and integration across multiple datasets and business domains.
• Develop and optimize SQL queries for processing, reporting, and analytical workloads.
• Support data platform operations, production processes, and ongoing enhancements.
• Explore enterprise datasets and support investigative analytics such as Fraud, Waste, and Abuse analysis.
• Implement data validation checks and ensure data quality, consistency, and completeness.
• Monitor pipeline performance, troubleshoot failures, and resolve data-related issues.
• Improve data models, workflows, and engineering processes.
• Support production and non-production environments.
• Participate in technical discussions, code reviews, documentation, and knowledge sharing.
• Collaborate with offshore and global delivery teams.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
• 3–5 years of experience in data engineering or related roles.
• Hands-on experience developing and supporting enterprise-scale data pipelines.
• Experience working with large datasets and complex data processing environments.
• Experience supporting analytics and reporting solutions.

Required Technical Skills

• Strong understanding of ETL/ELT concepts and data engineering practices.
• Strong SQL skills, including complex queries, query optimization, data transformation, and data validation.
• Experience with modern data platforms such as Snowflake or cloud-based data warehouses.
• Experience with distributed data processing frameworks, data lakes, and analytics platforms.
• Experience with data transformation frameworks and data modeling concepts.
• Ability to process and analyze structured and semi-structured data.
• Experience implementing data quality checks and validating datasets and analytical outputs.
• Strong debugging and troubleshooting skills.

Preferred Qualifications

• Experience supporting Fraud, Waste, and Abuse analytics or similar investigative data use cases.
• Healthcare domain experience with claims, provider, or clinical data.
• Familiarity with AI-enabled data engineering or analytics productivity tools.
• Experience with cloud data platforms and modern analytics ecosystems.
• Exposure to Airflow, Azure Data Factory, or similar orchestration tools.
• Experience in offshore delivery, managed services, or long-term production support environments.

Work Location

• Hybrid remote in Noida, Uttar Pradesh.

Role Benefits

• Work on enterprise-scale data engineering and analytics initiatives.
• Gain exposure to modern cloud data platforms and AI-enabled analytics capabilities.
• Collaborate with global data engineering, analytics, and business teams.
• Contribute to healthcare and business intelligence solutions.