Sr Data Engineer - AI & Industrial IoT Solutions
Apply NowCompany: Honeywell
Location: Atlanta, GA 30349
Description:
Join a team recognized for leadership, innovation and diversity
The future is what you make it.
Honeywell is seeking a Senior Data Engineer to join our dynamic global team in delivering cutting-edge AI/ML data products for industrial customers. In this role, you will design and implement scalable data pipelines, enabling next-generation AI solutions such as Large Language Models (LLMs), autonomous agents, and real-time inference systems. With a strong focus on IoT and real-time data processing, you will work at the intersection of industrial telemetry data and modern AI technologies to develop innovative, high-impact solutions. Join us in shaping the future of industrial AI.
LOCATION: Atlanta, GA
Are you ready to help make the future with us?
You will have the opportunity to work on challenging projects, leverage the latest AI technologies, and make a significant impact on optimizing operations and driving growth for our customers. The role offers professional growth, collaboration with experts, and the chance to be at the forefront of AI-driven industrial solutions.
BENEFITS:
Benefits provided may differ by role and location. Learn more at benefits.honeywell.com.
Unlimited Vacation Plan with No Preset Maximums
Flexible Hybrid Work Schedule
Medical/Rx Health Savings Account (HSA)
Dental/Vision
Short/Long-Term Disability
Employee Assistance Program (EAP)
401(k) Plan
Education Assistance
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
YOU MUST HAVE:
Bachelor's degree from an accredited institution in a technical discipline such as science, technology, engineering or mathematics
4+ years of data engineering experience with strong understanding of CDC, ELT/ETL workflows, streaming replication, and data quality frameworks
2+ years of hands-on experience with PySpark/Scala
2+ years of experience with cloud platforms (Azure/GCP/Databricks), particularly in implementing AI/ML data workflows
WE VALUE:
Strong understanding of data modeling for both analytical and AI workloads
Experience implementing RAG 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 with graph databases and knowledge graphs for AI applications
Experience working with distributed teams and cross-functional collaboration
Knowledge of data security and governance practices for AI systems
Expertise in version control systems, CI/CD methodologies
Experience working on analytics projects with Agile and Scrum Methodologies
Additional Information
The future is what you make it.
Honeywell is seeking a Senior Data Engineer to join our dynamic global team in delivering cutting-edge AI/ML data products for industrial customers. In this role, you will design and implement scalable data pipelines, enabling next-generation AI solutions such as Large Language Models (LLMs), autonomous agents, and real-time inference systems. With a strong focus on IoT and real-time data processing, you will work at the intersection of industrial telemetry data and modern AI technologies to develop innovative, high-impact solutions. Join us in shaping the future of industrial AI.
LOCATION: Atlanta, GA
Are you ready to help make the future with us?
You will have the opportunity to work on challenging projects, leverage the latest AI technologies, and make a significant impact on optimizing operations and driving growth for our customers. The role offers professional growth, collaboration with experts, and the chance to be at the forefront of AI-driven industrial solutions.
BENEFITS:
Benefits provided may differ by role and location. Learn more at benefits.honeywell.com.
Unlimited Vacation Plan with No Preset Maximums
Flexible Hybrid Work Schedule
Medical/Rx Health Savings Account (HSA)
Dental/Vision
Short/Long-Term Disability
Employee Assistance Program (EAP)
401(k) Plan
Education Assistance
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
YOU MUST HAVE:
Bachelor's degree from an accredited institution in a technical discipline such as science, technology, engineering or mathematics
4+ years of data engineering experience with strong understanding of CDC, ELT/ETL workflows, streaming replication, and data quality frameworks
2+ years of hands-on experience with PySpark/Scala
2+ years of experience with cloud platforms (Azure/GCP/Databricks), particularly in implementing AI/ML data workflows
WE VALUE:
Strong understanding of data modeling for both analytical and AI workloads
Experience implementing RAG 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 with graph databases and knowledge graphs for AI applications
Experience working with distributed teams and cross-functional collaboration
Knowledge of data security and governance practices for AI systems
Expertise in version control systems, CI/CD methodologies
Experience working on analytics projects with Agile and Scrum Methodologies
Additional Information
- Category: Engineering
- Location: 715 Peachtree Street, N.E., Atlanta, GA 30308 USA
- Exempt