Hiring for AWS Data Engineer ||

Aroha Technologies, Inc; Newark (NJ); Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Aroha Technologies, Inc is seeking an AWS Data Engineer to design, develop, and support AWS-based data pipelines for onboarding, transformation, and consumption of enterprise data. The role is onsite in Newark, NJ, three days per week, with a focus on Python/PySpark and AWS-native services.

You will ingest data from legacy sources, build ETL/ELT workflows, and optimize datasets for analytics and AI readiness. Strong emphasis on data quality, reliability, and collaboration with stakeholders.

Formación

• Experience designing and implementing scalable data pipelines on AWS.
• Hands-on with Python/PySpark and AWS-native data services.
• Ability to deliver reliable, analytics-ready datasets.

Responsabilidades

• Design, develop, and support AWS-based data pipelines for onboarding, transformation, and consumption of enterprise data.
• Ingest data from legacy and enterprise sources into AWS; build ETL/ELT workflows.
• Develop and optimize analytical datasets using Redshift and Athena; validate data quality.
• Collaborate with stakeholders and lead development of maintainable Python/PySpark code.

Conocimientos

Python

PySpark

SQL

AWS

Herramientas

AWS Glue

AWS EMR

S3

Redshift

Athena

Iceberg

DynamoDB

Descripción del empleo

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.