Senior Data Engineer, AWS Cloud | Java, Python & Databricks

MACHINE LEARNING TECHNOLOGIES LLC; Atlanta (GA); On-site; USD 120,000 - 160,000; Full time

Job summary

Machine Learning Technologies LLC in Atlanta is seeking a hands-on Senior Data Engineer to support a large-scale modernization and cloud migration. The role focuses on building Java/Spring Boot REST APIs and Python-based ETL pipelines on Databricks, deployed on AWS.

You will collaborate with backend engineers, data teams and DevOps to implement cloud-native solutions, containerization with Docker, Kubernetes orchestration, and robust testing, security, and performance practices.

Qualifications

• Hands-on Senior Java & Data Engineer with AWS-focused backend and data-pipeline experience.
• Solid background in Java/Spring Boot REST APIs and Python-based ETL/data pipelines on Databricks.
• Strong knowledge of cloud-native development, containers, microservices, and modern engineering practices.

Responsibilities

• Design, develop, and maintain scalable REST APIs and backend services with Java and Spring Boot.
• Build modular microservices and API-first architectures for large-scale modernization.
• Develop Python-based ETL/data pipelines on Databricks and integrate with AWS services.
• Containerize applications with Docker and deploy via Kubernetes, including CI/CD and monitoring.
• Collaborate with architects, data engineers, and DevOps throughout the lifecycle.

Skills

Java Spring Boot REST APIs

Python ETL development

Microservices API-driven architectures

Docker and Kubernetes

Unit/Integration/API Testing

Bitbucket and CI/CD

Software engineering fundamentals

Tools

Databricks

PySpark

Apache Spark

Docker

Kubernetes

CI/CD tooling

Job description

Machine Learning Technologies LLC in Atlanta is seeking a hands-on Senior Data Engineer to support a large-scale modernization and cloud migration. The role focuses on building Java/Spring Boot REST APIs and Python-based ETL pipelines on Databricks, deployed on AWS.

You will collaborate with backend engineers, data teams and DevOps to implement cloud-native solutions, containerization with Docker, Kubernetes orchestration, and robust testing, security, and performance practices.