Data Engineer

Honeywell•Atlanta, GA

13d•Hybrid

About The Position

As a Data Engineer, you will be part of a high-performing global team delivering AI- and data-driven solutions for Honeywell’s industrial customers, with a focus on IoT and real-time data processing. In this role, you will architect and implement scalable data pipelines and platforms that enable advanced analytics and AI capabilities, including large-scale machine learning models, intelligent automation, and real-time inference. You will work closely with cross-functional engineering and product teams at the intersection of IoT telemetry and modern data technologies to develop reliable, high-impact industrial solutions. You will report directly to our Data Engineering Manager and you’ll work out of our Atlanta, GA location on a Hybrid work schedule. Note: for the first 90 days, new hires must be prepared to work 100% onsite M-F. Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

Responsibilities

Design and implement scalable data architectures to process high-volume IoT sensor data and telemetry streams, ensuring reliable data capture and processing for AI/ML workloads
Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows
Develop and optimize RAG (Retrieval Augmented Generation) systems, including vector databases, embedding pipelines, and efficient retrieval mechanisms
Create robust data integration solutions that combine industrial IoT data streams with enterprise data sources for AI model training and inference
Implement DataOps practices to ensure continuous integration and delivery of data pipelines powering AI solutions
Design and maintain automated testing frameworks for data quality, data drift detection, and AI model performance monitoring
Create self-service data assets enabling data scientists and ML engineers to access and utilize data efficiently
Design and maintain automated documentation systems for data lineage and AI model provenance
Partner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment
Drive continuous improvement in data engineering practices and tooling
Establish best practices for data pipeline development and maintenance in AI contexts
Drive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape