Description

• 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

Key Skills

Education

Bachelor's degree