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# Data Platform Engineer

- **Company:** Intersources Inc.
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Amazon S3, Batch Processing, Big Data, Cloud Computing, Computer Programming, Data as a Services, Data Infrastructure, Extract Transform Load (ETL), Github, Python (Programming Language), Software Engineering, Backend, Data Lakes, Infrastructure Automation Frameworks, Data Pipelines, Databricks, Data Generation
- **Published:** October 27, 2026
- **Apply:** https://www.careerjet.com/job/us578de7058c81efd49d05d9722f4bee1d/eaa

## About the Role

· 5-8 years of experience in software development, with a strong focus on backend or platform engineering.
· Proven experience with AWS data services and data lake architectures.
· Strong programming skills in Python.
· Hands-on experience with GitHub CI/CD and infrastructure-as-code practices.
· Demonstrated ability to optimize cloud infrastructure for scalability, performance, and cost-efficiency.
· Excellent communication skills and a collaborative mindset.
Nice to Have
· Experience with Databricks or similar unified analytics platforms.
· Familiarity with synthetic data generation techniques.
· Exposure to data platform architecture or experience in a platform architect role.

## Description

We are seeking a highly skilled Data Platform Engineer to design, build, and optimize scalable data infrastructure that powers AI and advanced analytics initiatives. This role is not about building data pipelines for analytics, but about engineering the foundational platform that enables teams to deliver AI-ready data efficiently and securely., · Design and implement scalable data platform components using AWS Step Functions, Glue, S3, Lake Formation, Iceberg, and Athena.
· Develop and maintain robust ETL and batch processing frameworks to support large-scale data ingestion and transformation.
· Write clean, efficient, and testable code in Python to support platform services and automation.
· Build and maintain CI/CD pipelines using GitHub Actions or similar tools to ensure reliable and repeatable deployments.
· Optimize cloud infrastructure for performance and cost, leveraging best practices in cloud-native architecture.
· Document platform architecture, processes, and operational runbooks.