About the role

Data Engineer delivering AI- and data-driven solutions for Honeywell’s industrial customers. Architecting and implementing scalable data pipelines and platforms focused on IoT and real-time data processing.

Responsibilities

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 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
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 GitHub 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, 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.