Senior AWS Data Engineer

EXL

Senior AWS Data Engineer managing data engineering projects ensuring alignment with business objectives and overseeing data engineers. Designing and maintaining scalable data pipelines in AWS cloud environment.

Posted 7/4/2026full-timeRemote • 🇮🇳 IndiaSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudDynamoDBETLKafkaPythonShell ScriptingSparkSQL

About the role

Key responsibilities & impact

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.
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.

Requirements

What you’ll need

Bachelor’s 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.

Benefits

Comp & perks

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Hard Skills & Tools

Data LakesData WarehousesData ApplicationsAWS GlueAWS RedshiftAWS S3AWS LambdaSQLPythonShell Scripting

Soft Skills

Problem-SolvingAttention to DetailCommunicationTeamwork