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

  • 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.

Key Responsibilities

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.

AWS Data Engineering

  • 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.

Data Quality & Reliability

  • 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.

Development & Delivery

  • 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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