
Data Engineer
Role summary
Honeywell is seeking a Data Engineer to join a global team focused on AI and data-driven solutions for industrial customers, emphasizing IoT and real-time data processing. The role involves architecting and implementing scalable data pipelines and platforms to support advanced analytics, machine learning, and real-time inference. Key responsibilities include designing data architectures for high-volume IoT data, building AI product lifecycle pipelines, developing RAG systems, and implementing DataOps practices. The position requires a minimum of 3 years of data engineering experience, proficiency in Databricks, PySpark, cloud platforms (Azure, GCP), and CI/CD tools. This is a hybrid role based in Atlanta, GA, with an initial 90-day onsite requirement.
Job Description
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.
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
- 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 systems 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
- 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
Qualifications
YOU MUST HAVE
- Minimum 3 years of experience in data engineering with a strong grasp of Change Data Capture (CDC), ELT/ETL workflows, streaming replication, and data quality frameworks
- Deep expertise in building scalable data pipelines using Databricks, including Unity Catalog and Delta Live Tables
- Strong hands-on proficiency with PySpark for distributed data processing and transformation
- Solid 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 GitHub Actions, Bitbucket, Bamboo, and Octopus Deploy to automate and manage data pipeline deployments.
WE VALUE
- Experience building solutions on RAG and Agentic architectures 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
Benefits Of Working For 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 information visit: click here
About Honeywell
Honeywell International Inc. (NYSE: HON) invents and commercializes technologies that address some of the world’s most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high growth businesses in broad-based, attractive industrial end markets. Our products and solutions enable a safer, more comfortable, and more productive world, enhancing the quality of life of people around the globe. Learn more here: https://www.honeywell.com/us/en
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: 4/3/2026
About Us
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.
Sample Honeywell interview questions
- 1
Create a real-time translation service for text and speech.
system designmedium - 2
Maximum Subarray Sum Find the maximum subarray sum in an integer array. Input: nums = [-3,-4,-1,-2] Output: -1 Explanation: Kadane's algorithm correctly identifies that the single isolated element -1 provides the highest possible sum.
codingmedium
Sign up for a personalized interview prep pack tailored to this role.
Similar roles
- Senior Data EngineerExperion Technologies · Plano, Texas, United States · Hybrid
- Lead Data EngineerSmart IT Frame LLC · Los Angeles, California, United States · Hybrid
Principal Data EngineerRS21: A Data Science and Visualization Company · United States · Remote
Senior Data EngineerRaag Solutions · Bellevue, Washington, United States · Onsite- Lead Data EngineerRetail Insight Ltd · Illinois, United States · Hybrid