Full Time
Hiring for AWS Data Engineer || Newark NJ Hybrid || Full-time
Aroha Technologies, Inc
Full-time
Mid-Senior level
Banking, Wellness and Fitness Services, and Hospitals and Health Care
Newark, NJ
???* / year
Job Description
Job Role: AWS Data Engineer
Location: Newark NJ Onsite 3 days/ week
Type: Full-time
Interview: CLIENT INTERVIEW will happen F2F in Newark, NJ too.
Role Summary
Data Pipeline Development
Location: Newark NJ Onsite 3 days/ week
Type: Full-time
Interview: CLIENT INTERVIEW will happen F2F in Newark, NJ too.
Role Summary
- Design, develop and support AWS-based data pipelines for enterprise data onboarding, transformation, and consumption. The engineer will work hands-on with Python/PySpark and AWS-native data services to create reliable, scalable, and analytics/AI-ready datasets.
Data Pipeline Development
- Develop ingestion and transformation pipelines using AWS Glue, EMR, S3, Python and PySpark.
- Ingest data from legacy and enterprise source systems into AWS.
- Develop transformations required to create standardized, consumption-ready datasets.
- Build and maintain ETL/ELT workflows supporting enterprise data domains.
- Work with S3 for data storage and processing.
- Develop workloads using Glue and EMR.
- Develop and optimize analytical datasets and workloads involving Redshift and Athena.
- Support Iceberg-based data structures on S3 where required.
- Work with other AWS data services such as DynamoDB when appropriate to the solution.
- Implement data validation, reconciliation, and quality checks within pipelines.
- Identify and resolve data-processing failures and data-quality issues.
- Support performance optimization of PySpark/SQL workloads and data pipelines.
- Participate in troubleshooting and production support.
- Develop clean, maintainable, and reusable Python/PySpark code.
- Perform unit and integration testing.
- Participate in peer code reviews.
- Document data flows, mappings, transformations, and technical designs.
- Work with the Data Engineering Lead and client stakeholders to understand source data and transformation requirements.
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