Data Engineer

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) match,…