Innovate to solve the world’s most important challenges
The Lead Data Engineer role will be part of a high-performing global team that delivers cutting-edge AI/ML data products for Honeywell’s Industrial customers, with a specific focus on IoT and real-time data processing. As a data engineer, you will architect and implement scalable data pipelines that power next-generation AI solutions, including Large Language Models (LLMs), autonomous agents, and real-time inference systems. You will work at the intersection of IoT telemetry data and modern AI technologies to create innovative industrial solutions.

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
• Architect and build 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
• Design and implement robust data integration solutions that combine industrial IoT data streams with enterprise data sources for AI model training and inference

DataOps & Governance:

• Define a mature DataOps strategies to ensure continuous integration and delivery of data pipelines powering AI solutions
• Lead efforts in data quality, observability, and lineage tracking to maintain high integrity in AI/ML datasets.
• 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
• Ensure compliance with data governance policies, including security, privacy, and regulatory requirements for AI-driven applications

Technical Leadership & Innovation:

• Lead architectural discussions, establish standards and drive technical excellence across teams
• Partner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment
• Mentor data engineers on standards, best practices, and innovative approaches to build extensible and reusable solution
• Drive innovation, continuous improvement in data engineering practices and tooling
• Manage stakeholder expectations, aligning data engineering roadmaps with business and AI strategy