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.