Senior AWS Data Engineer

Overview:

We are seeking experienced AWS Data Engineers to design, implement, and maintain robust data pipelines and analytics solutions using AWS services. The ideal candidate will have a strong background in AWS data services, big data technologies, and programming languages.

• Exp- 3 to 7 years
• Location- Bangalore, Pune, Hyderabad, Coimbatore, Delhi NCR, Mumbai
  
  
  

Requirements

Key Responsibilities:

• Design and implement scalable, high-performance data pipelines using AWS services
• Develop and optimize ETL processes using AWS Glue, EMR, and Lambda
• Build and maintain data lakes using S3 and Delta Lake
• Create and manage analytics solutions using Amazon Athena and Redshift
• Design and implement database solutions using Aurora, RDS, and DynamoDB
• Develop serverless workflows using AWS Step Functions
• Write efficient and maintainable code using Python/PySpark, and SQL/PostgrSQL
• Ensure data quality, security, and compliance with industry standards
• Collaborate with data scientists and analysts to support their data needs
• Optimize data architecture for performance and cost-efficiency
• Troubleshoot and resolve data pipeline and infrastructure issues
  
  

Required Qualifications:

• bachelor's degree in computer science, Information Technology, or related field
• Relevant years of experience as a Data Engineer, with at least 60% of experience focusing on AWS
• Strong proficiency in AWS data services: Glue, EMR, Lambda, Athena, Redshift, S3
• Experience with data lake technologies, particularly Delta Lake
• Expertise in database systems: Aurora, RDS, DynamoDB, PostgreSQL
• Proficiency in Python and PySpark programming
• Strong SQL skills and experience with PostgreSQL
• Experience with AWS Step Functions for workflow orchestration
  
  

Technical Skills:

• AWS Services: Glue, EMR, Lambda, Athena, Redshift, S3, Aurora, RDS, DynamoDB, Step Functions
• Big Data: Hadoop, Spark, Delta Lake
• Programming: Python, PySpark
• Databases: SQL, PostgreSQL, NoSQL
• Data Warehousing and Analytics
• ETL/ELT processes
• Data Lake architectures
• Version control: Git
• Agile methodologies