招聘
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:
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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
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Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows
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Develop and optimize RAG (Retrieval Augmented Generation) systems, including vector databases, embedding pipelines, and efficient retrieval mechanisms
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Create robust data integration solutions that combine industrial IoT data streams with enterprise data sources for AI model training and inference
Data Ops:
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Implement Data Ops practices to ensure continuous integration and delivery of data pipelines powering AI solutions
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Design and maintain automated testing frameworks for data quality, data drift detection, and AI model performance monitoring
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Create self-service data assets enabling data scientists and ML engineers to access and utilize data efficiently
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Design and maintain automated documentation systems for data lineage and AI model provenance
Collaboration & Innovation:
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Partner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment
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Drive continuous improvement in data engineering practices and tooling
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Establish best practices for data pipeline development and maintenance in AI contexts
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Drive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape
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 Py Spark 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
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Honeywell
PublicHoneywell International Inc. is an American publicly traded, multinational conglomerate corporation headquartered in Charlotte, North Carolina. It primarily operates in four areas of business: aerospace, building automation, industrial automation, and energy and sustainability solutions (ESS).
10,001+
员工数
Charlotte
总部位置
$130B
企业估值
评价
2.3
2条评价
工作生活平衡
2.5
薪酬
3.5
企业文化
2.0
职业发展
2.0
管理层
1.5
15%
推荐给朋友
优点
Good compensation potential
Competitive pay scale
缺点
Poor communication from recruiters
Inadequate safety training
Poor management response to incidents
薪资范围
901个数据点
Mid/L4
Senior/L5
Mid/L4 · Data Analyst II
2份报告
$136,600
年薪总额
基本工资
$105,077
股票
-
奖金
-
$136,600
$136,600
面试经验
3次面试
难度
3.0
/ 5
时长
14-28周
录用率
33%
体验
正面 0%
中性 33%
负面 67%
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Interview
4
Assessment/Testing
5
Final Interview
6
Offer
常见问题
Technical Knowledge
Behavioral/STAR
Past Experience
Problem Solving
Culture Fit
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