T&T - EAD- Engg- Senior Consultant & Consultant | 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 & Mumbai

Key Responsibilities:

1. Design and implement scalable, high-performance data pipelines using AWS services

2. Develop and optimize ETL processes using AWS Glue, EMR, and Lambda

3. Build and maintain data lakes using S3 and Delta Lake

4. Create and manage analytics solutions using Amazon Athena and Redshift

5. Design and implement database solutions using Aurora, RDS, and DynamoDB

6. Develop serverless workflows using AWS Step Functions

7. Write efficient and maintainable code using Python/PySpark, and SQL/PostgrSQL

8. Ensure data quality, security, and compliance with industry standards

9. Collaborate with data scientists and analysts to support their data needs

10. Optimize data architecture for performance and cost-efficiency

11. Troubleshoot and resolve data pipeline and infrastructure issues

Required Qualifications:

1. bachelor’s degree in computer science, Information Technology, or related field

2. Relevant years of experience as a Data Engineer, with at least 60% of experience focusing on AWS

3. Strong proficiency in AWS data services: Glue, EMR, Lambda, Athena, Redshift, S3

4. Experience with data lake technologies, particularly Delta Lake

5. Expertise in database systems: Aurora, RDS, DynamoDB, PostgreSQL

6. Proficiency in Python and PySpark programming

7. Strong SQL skills and experience with PostgreSQL

8. 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