KEY 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

---

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.

---

As a Senior Advanced Data Engineer here at Honeywell, you will be part of a high-performing global team that delivers cutting-edge AI/ML data products for Honeywell's Industrial customers, with a specific focus on IoT and real-time data processing. As a data engineer, you will architect and implement scalable data pipelines that power next-generation AI solutions, including Large Language Models (LLMs), autonomous agents, and real-time inference systems. You will report directly to our Sr Data Engineering Manager, and you’ll work out of our Atlanta, GA location on a Hybrid work schedule.

---

YOU MUST HAVE

• Education- Master's degree in computer science, Engineering, Applied Mathematics or related STEM field
• 5 plus years building production data pipelines in Databricks processing TB scale data
• 3 plus years experience implementing medallion architecture (Bronze/Silver/Gold) with Delta Lake, Delta Live Tables (DLT), and Lakeflow for batch and streaming pipelines from Event Hub or Kafka sources
• 3 plus years experience and hands-on proficiency with PySpark for distributed data processing and transformation
• 3 plus years experience working with cloud platforms such as Azure, GCP and Databricks, especially in designing and implementing AI/ML-driven data workflows
• Proficient in CI/CD practices using Databricks Asset Bundles (DAB), Git workflows, GitHub Actions, and understanding of DataOps practices including data quality testing and observability
• Hands-on experience building RAG applications with vector databases, LLM integration, and agentic frameworks like LangChain, LangGraph

WE VALUE

• Experience building RAG and agentic architecture solutions and working with LLM-powered applications
• Expertise in real-time data processing frameworks (Apache Spark Streaming, Structured Streaming)
• Knowledge of MLOps practices and experience building data pipelines for AI model deployment
• Experience with time-series databases and IoT data modeling patterns
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for AI workloads
• Strong background in data quality implementation for AI training data
• Experience working with distributed teams and cross-functional collaboration
• Knowledge of data security and governance practices for AI systems
• Experience working on analytics projects with Agile and Scrum Methodologies
• Natural analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scale

BENEFITS AT HONEYWELL:

In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays .For more Honeywell Benefits information visit: https://benefits.honeywell.com/

The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Job Posting Date: 12/18/2025