Hiring for AWS Data Engineer ||

Aroha Technologies, Inc; Newark (NJ); On-site; USD 120,000 - 160,000; Full time

Job summary

Aroha Technologies, Inc. is seeking an AWS Data Engineer to design, develop, and operate AWS-based data pipelines for onboarding, transformation, and analytics-ready datasets. The role focuses on Python/PySpark development and hands-on work with AWS data services in Newark, NJ onsite three days per week.

You will implement robust data quality checks, optimize PySpark/SQL workloads, and collaborate with stakeholders to deliver reliable, scalable data solutions for analytics and AI readiness.

Qualifications

• Experience building AWS-based data pipelines.
• Proficiency with Python and PySpark.
• Familiarity with AWS data services (Glue, EMR, S3, Redshift, Athena).

Responsibilities

• Design, develop and support AWS-based data pipelines for onboarding, transformation and consumption.
• Build and maintain ETL/ELT workflows across data domains.
• Develop clean Python/PySpark code and test.
• Participate in peer code reviews and document data flows.

Skills

Python

PySpark

AWS

Data pipelines

ETL/ELT

SQL

Data quality

Troubleshooting

Code reviews

Tools

AWS Glue

AWS EMR

S3

Redshift

Athena

Iceberg

DynamoDB

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.