Educational Requirements

Bachelor of Engineering,BCom,BBA

Service Line

Data Analytics Unit

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, 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.

Technical and Skilled Requirements:
Deep hands-on experience with AWS Glue, including job orchestration, Glue Studio/Jobs, and integration with other AWS data services.
Robust 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 robust plus.
Ability to work in cross-functional teams, communicate technical concepts clearly, and mentor junior engineers on AWS data best practices.

Preferred Skills:
Technology->Cloud Platform->AWS Database

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