Sr Advanced Data Engineer

Honeywell•Atlanta, GA

15d

About The Position

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

Data Engineering & AI Pipeline Development:
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
Lead the architecture and development of scalable data platforms on Databricks
Drive the integration of GenAI capabilities into data workflows and applications
Optimize data processing for performance, cost, and reliability at scale
Create robust data integration solutions that combine industrial IoT data streams with enterprise data sources for AI model training and inference
DataOps:
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 for data lineage and AI model provenance
Collaboration & Innovation:
Partner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment
Mentor team members and provide technical leadership on complex data engineering challenges
Establish data engineering best practices, including modular code design and reusable frameworks
Drive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape