AWS Data Engineer

Interon IT Solutions; Chantilly (VA); On-site; USD 125,000 - 170,000; Full time

Job summary

Interon IT Solutions is seeking an experienced AWS Data Engineer to design, build, and support scalable data pipelines in a cloud environment. You will collaborate with engineering, analytics, and business teams to process large datasets and deliver reliable, production-ready data solutions.

Ideal candidates have 7+ years in data engineering, strong Python and PySpark skills, ETL development, and hands-on AWS experience, along with SQL and data modeling capabilities.

Qualifications

• 7+ years of data engineering experience.
• Strong hands-on experience with Python, PySpark, ETL, and AWS.
• Strong SQL and data-transformation skills.
• Experience processing large datasets in distributed environments.
• Understanding of data modeling and cloud-based data-pipeline architecture.
• Experience troubleshooting and supporting production data pipelines.
• Strong communication and problem-solving skills.

Responsibilities

• Design, develop, and maintain scalable ETL and data-processing pipelines.
• Build data-engineering solutions using Python and PySpark.
• Process and transform large structured and unstructured datasets.
• Develop cloud-based data solutions using AWS services.
• Monitor data pipelines and troubleshoot data-quality and performance issues.
• Optimize ETL workflows for reliability, scalability, and efficiency.
• Partner with application, analytics, and business teams to understand data requirements.
• Perform unit testing, code reviews, deployments, and production support.
• Follow data security, governance, and engineering best practices.
• Document technical designs, workflows, and operational procedures.

Skills

Python

PySpark

ETL

AWS

SQL

Data modeling

Distributed processing

Tools

Databricks

Job description

JD: Client is seeking an experienced AWS Data Engineer to design, build, and support scalable cloud-based data pipelines. The ideal candidate will have strong hands-on experience with Python, PySpark, ETL development, and AWS data services. This person will work with engineering, analytics, and business teams to process large datasets and deliver reliable, production-ready data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL and data-processing pipelines.
• Build data-engineering solutions using Python and PySpark.
• Process and transform large structured and unstructured datasets.
• Develop cloud-based data solutions using AWS services.
• Monitor data pipelines and troubleshoot data-quality and performance issues.
• Optimize ETL workflows for reliability, scalability, and efficiency.
• Partner with application, analytics, and business teams to understand data requirements.
• Perform unit testing, code reviews, deployments, and production support.
• Follow data security, governance, and engineering best practices.
• Document technical designs, workflows, and operational procedures.

Required Qualifications

• Minimum 7 years of data engineering experience.
• Strong hands-on experience with Python, PySpark, ETL, and AWS.
• Strong SQL and data-transformation skills.
• Experience processing large datasets in distributed environments.
• Understanding of data modeling and cloud-based data-pipeline architecture.
• Experience troubleshooting and supporting production data pipelines.
• Strong communication and problem-solving skills.

Preferred Qualifications

• Hands-on Databricks experience.
• Experience with cloud-based data lakes and distributed data-processing platforms.