AWS Data Engineer

Infosys; Maharashtra; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Infosys is seeking an experienced data engineer to design, develop, and maintain scalable data pipelines on AWS for analytics and BI. You will implement data warehousing on Redshift, enforce data quality, and collaborate with cross-functional teams to deliver reliable data solutions.

The role emphasizes governance, security, and best practices, with opportunities to mentor junior engineers and contribute to reusable AWS data frameworks.

Qualifications

• Deep hands-on experience with AWS Glue, including job orchestration, Glue Studio/Jobs, and integration with other AWS data services.
• Strong experience with AWS Redshift, including cluster management, query optimization, workload management, and cost/performance tuning.
• Demonstrated track record of delivering end-to-end data engineering solutions on AWS for analytics, BI, or data science use cases.
• Experience implementing data governance, metadata management, and data cataloging using AWS-native or similar tools.
• AWS-related certification (e.g., AWS Certified Data Analytics, AWS Certified Solutions Architect, or AWS Certified Developer) is a strong plus.
• Ability to work in cross-functional teams, communicate technical concepts clearly, and mentor junior engineers on AWS data best practices.
• Design, develop, and maintain scalable ETL/ELT data pipelines on AWS to ingest, transform, and load data from diverse sources.
• Build and optimize data workflows using AWS Glue, ensuring reliability, reusability, and performance of jobs and crawlers.
• Design, implement, and manage data warehousing solutions on AWS Redshift, including schema design, partitioning, and performance tuning.
• Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and consistency of data assets.
• Collaborate with analytics and business teams to understand reporting and BI requirements and translate them into efficient data models and datasets.
• Apply best practices for security, compliance, and governance across AWS data services, including access control and encryption standards.
• Troubleshoot and resolve data pipeline issues, optimize job performance, and proactively improve system reliability and scalability.
• Contribute to documentation, standards, and reusable frameworks for data engineering on AWS to support team-wide efficiency and consistency.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines on AWS to ingest, transform, and load data from diverse sources.
• Build and optimize data workflows using AWS Glue, ensuring reliability and performance of jobs and crawlers.
• Design and manage data warehousing solutions on AWS Redshift with optimized schemas and partitioning.
• Implement data quality checks and monitoring to ensure data accuracy and completeness.
• Collaborate with analytics teams to translate BI requirements into data models and datasets.
• Troubleshoot data pipelines and improve system reliability and scalability.

Skills

AWS data engineering

ETL/ELT design

Data governance

Data modeling

Cross-functional collaboration

Mentor junior engineers

Tools

AWS Glue

AWS Redshift

Job description

Responsibilities & Qualifications

• Deep hands‑on experience with AWS Glue, including job orchestration, Glue Studio/Jobs, and integration with other AWS data services.
• Strong experience with AWS Redshift, including cluster management, query optimization, workload management, and cost/performance tuning.
• Demonstrated track record of delivering end‑to‑end data engineering solutions on AWS for analytics, BI, or data science use cases.
• Experience implementing data governance, metadata management, and data cataloging using AWS‑native or similar tools.
• AWS‑related certification (e.g., AWS Certified Data Analytics, AWS Certified Solutions Architect, or AWS Certified Developer) is a strong plus.
• Ability to work in cross‑functional teams, communicate technical concepts clearly, and mentor junior engineers on AWS data best practices.
• Design, develop, and maintain scalable ETL/ELT data pipelines on AWS to ingest, transform, and load data from diverse sources.
• Build and optimize data workflows using AWS Glue, ensuring reliability, reusability, and performance of jobs and crawlers.
• Design, implement, and manage data warehousing solutions on AWS Redshift, including schema design, partitioning, and performance tuning.
• Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and consistency of data assets.
• Collaborate with analytics and business teams to understand reporting and BI requirements and translate them into efficient data models and datasets.
• Apply best practices for security, compliance, and governance across AWS data services, including access control and encryption standards.
• Troubleshoot and resolve data pipeline issues, optimize job performance, and proactively improve system reliability and scalability.
• Contribute to documentation, standards, and reusable frameworks for data engineering on AWS to support team‑wide efficiency and consistency.

Infosys provides equal employment opportunities to applicants and employees without regard to race, color, sex, gender identity; sexual orientation, religious practices and observances; national origin; pregnancy, childbirth, or related medical conditions; status as a protected veteran or spouse/family member of a protected veteran; or disability.