Data Engineer

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.

• 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

• Implement Data Ops 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

• 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

• 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 Git Hub Actions, Bitbucket, Bamboo, and Octopus Deploy to automate and manage data pipeline deployments.

• 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

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)…