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# Senior AWS Data Engineer

- **Company:** JSR Tech Consulting
- **Location:** Harrison, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Confluence, JIRA, Automation of Tests, Cloud Engineering, Code Review, Information Systems, Databases, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, Relational Databases, DevOps, File Systems, Amazon DynamoDB, Gradle, Groovy, Identity and Access Management, Python (Programming Language), Apache Maven, Network Attached Storage (Server Appliance), Systems Development Life Cycle, Role-Based Access Control, Cloud Services, Shell Script, Software Engineering, SonarQube, SQL Databases, Enterprise Data Management, Cloudbees, Scripting, Data Ingestion, Apache Spark, Cloudformation, Data Lakes, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, AWS Data Analytics, Apache Kafka, Bitbucket, Cloudwatch, Amazon Simple Queue Service (SQS), Data Pipelines, Docker, Jenkins, Vulnerability Analysis, Artifactory
- **Published:** October 7, 2026
- **Apply:** https://www.disabledperson.com/jobs/75813579-senior-aws-data-engineer

## About the Role

This is an excellent opportunity for an experienced AWS Data Engineer who enjoys building scalable data pipelines, automating infrastructure, and working across cloud engineering, DevOps, and enterprise data platforms., * Bachelor's degree in Computer Science, Information Systems, Software Engineering, or related field.

 * 8+ years of Data Engineering experience.
 * AWS Solutions Architect or AWS Developer Certification (required).
 * Strong experience building cloud-native data platforms on AWS.
 * Experience developing enterprise data ingestion pipelines.
 * Strong Python, SQL, and Shell scripting experience.
 * Experience with AWS Data Lake and Data Warehouse architectures.
 * Experience implementing ETL/ELT solutions.
 * Strong DevOps experience with CI/CD automation.

## Description

Join a highly visible Enterprise Data Platform team responsible for designing and building cloud-native data solutions supporting enterprise analytics, governance, and modern data architecture initiatives. You'll work in a cutting-edge AWS environment leveraging advanced data engineering, DevOps, and data governance technologies., * Design, develop, and maintain scalable AWS-based data engineering solutions.

 * Build automated data ingestion pipelines from databases, APIs, file systems, and NAS shares into AWS data lakes and relational databases.
 * Develop end-to-end data solutions including ingestion, storage, processing, integration, and data access.
 * Design and implement modern ETL/ELT pipelines.
 * Build enterprise-scale AWS Data Lake and Data Warehouse solutions.
 * Develop infrastructure using Infrastructure as Code (CloudFormation).
 * Build and maintain CI/CD pipelines using Jenkins and CloudBees.
 * Implement DevOps best practices across AWS environments.
 * Create automated deployment pipelines integrating SonarQube, security scans, and automated testing.
 * Collaborate with architecture, QA, product, and engineering teams throughout the SDLC.
 * Participate in Agile ceremonies including estimation, planning, code reviews, and sprint execution.
 * Troubleshoot application and pipeline performance issues.
 * Document technical designs, implementation decisions, issues, and lessons learned.
 * Stay current with emerging AWS technologies and engineering best practices., Hands-on experience with AWS services including:
 * CloudFormation
 * S3
 * Athena
 * Glue
 * Glue DataBrew
 * EMR / Spark
 * RDS
 * Aurora
 * Redshift
 * DynamoDB
 * Lambda
 * Step Functions
 * IAM
 * KMS
 * Secrets Manager
 * EventBridge
 * EC2
 * SQS
 * SNS
 * Lake Formation
 * CloudWatch
 * CloudTrail
 * DataSync
 * DMS

Preferred Experience

 * Amazon Kinesis
 * AWS Managed Airflow
 * AWS Managed Kafka (MSK)
 * Redshift RBAC
 * AWS IAM security models
 * Jenkins
 * CloudBees
 * Bitbucket
 * Docker
 * Maven
 * Gradle
 * MS Build
 * SonarQube
 * Artifactory
 * Jira
 * Confluence
 * Groovy scripting
 * API deployment automation