EXL - Senior AWS Data Engineer

: Manage data engineering projects, ensuring alignment with business objectives. Provide strategic guidance on data engineering best practices. Oversee a team of data engineers. Ensure continuous improvement of data processes. Key Responsibilities : - Design, build, and maintain efficient, reusable, and reliable architecture and code for data pipelines and data applications on AWS. - Build robust data ingestion pipelines (from on-prem to AWS and within AWS) using AWS services such as Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, and SQS. - Develop and manage ETL/ELT processes to collect, process, and store data from multiple sources, ensuring data quality, integrity, and security. - Architect and implement end-to-end data solutions (ingestion, storage, integration, processing, access) on AWS, with a focus on data lakes and data warehouses. - Participate in the architecture and system design discussions for high-scale data engineering projects. - Independently perform hands-on development, unit testing, and participate in code reviews to ensure adherence to best practices. - Implement serverless applications using AWS Lambda, API Gateway, Step Functions, and other AWS technologies. - Migrate data from traditional relational databases, file systems, and APIs to AWS-based data lakes (S3), RDS, Aurora, and Redshift. - Implement high-velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred). - Architect and implement CI/CD strategies for enterprise data platforms. - Collaborate with product, operations, QA, and cross-functional teams throughout the software development cycle. - Stay abreast of new technology developments, implement POCs for new tools/technologies, and onboard them for real-world use cases. - Identify and resolve performance issues and continuously optimize for cost, reliability, and scalability. Required Qualifications : - Bachelors degree in Computer Science, Software Engineering, MIS, or equivalent combination of education and experience. - 5+ years of experience implementing and supporting data lakes, data warehouses, and data applications on AWS for large enterprises. - Strong programming experience with Python, Shell scripting, and SQL. - Solid experience with AWS services : CloudFormation, S3, Athena, Glue, EMR/Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, Secrets Manager. - Experience in serverless application development and data pipeline orchestration. - Experience in system analysis, design, development, and implementation of data ingestion pipelines in AWS. - Knowledge of ETL/ELT, data modeling, and big data technologies. - Familiarity with data warehousing concepts and cloud-based architecture. - Strong problem-solving skills and attention to detail. - Excellent communication and teamwork abilities.