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# Data Engineer

- **Company:** Insight Global
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Microsoft Azure, Continuous Integration, Information Engineering, Data Governance, Data Warehousing, DevOps, Python (Programming Language), Machine Learning, SQL Databases, Cloud Platform System, State Machines, AWS Lambda, Cloudformation, Containerization, Data Lakes, Pyspark, AWS Glue, Data Analytics, Apache Kafka, Data Management, Terraform, Data Pipelines, Docker
- **Published:** August 11, 2026
- **Apply:** https://www.juju.com/job/00000000gmn5z3

## About the Role

* 7-10+ years of experience in Data Engineering with a strong focus on AWS Cloud

 * Proficiency in Python, SQL, and PySpark.

 * Hands-on experience with the following

 * AWS Glue

 * Apache Kafka

 * Amazon S3

 * AWS Lambda

 * AWS Step Functions

 * CloudFormation Templates (CFT)/Terraform

 * Basic understanding of AI concepts

 * Experience with CI/CD tools and DevOps practices in cloud environments.

 * Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. * AWS certifications (e.g., AWS Certified Data Analytics, Solutions Architect)

 * Experience with other cloud platforms (Azure, GCP)

 * Familiarity with containerization (Docker, ECS) and orchestration tools (Airflow, Step functions)

 * Knowledge of data governance frameworks and compliance standards

## Description

* Design, build, and optimize scalable data pipelines using AWS Glue, Apache Kafka, AWS Lambda, and Step Functions.

 * Develop and maintain robust data lakes and data warehouses on Amazon S3.

 * Write infrastructure as code using CloudFormation Templates (CFT).

 * Collaborate with Data Scientists and ML Engineers to integrate AI/ML models into production-grade data workflows.

 * Write efficient, reusable, and testable code in Python, SQL, and PySpark.

 * Ensure data quality, governance, and security across all data platforms.

 * Monitor and troubleshoot data pipeline performance and reliability.

 * Participate in architectural discussions and contribute to cloud strategy and best practices.


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