As a Principal Data Engineer here at Honeywell, you will play a crucial role in designing and implementing advanced data solutions for AI solutions that drive business insights, enhance decision-making processes and empower AI solutions. Your expertise will help in critical data science development activities across all AI modalities (classic, Gen and agentic) and data types (structured and unstructured).

You will report directly to our AI Director and you’ll work out of our Phoenix, AZ location on a Hybrid work schedule.

In this role, you will impact the organization by leveraging your technical skills to develop innovative data solutions that support strategic initiatives and improve operational efficiency.

At Honeywell, our people leaders play a critical role in developing and supporting our employees to help them perform at their best and drive change across the company. Help to build a strong, diverse team by recruiting talent, identifying and developing successors, driving retention and engagement, and fostering an inclusive culture.

• Bachelor’s degree in a technical field (CS, Engineering, Math, or related).

• Experience supporting AI at scale across classic ML, GenAI/LLM, and agentic AI systems.

• Experience with vector databases and semantic search (Databricks Vector Search, Pinecone, FAISS, Milvus, Open Search).

• Familiarity with LLM and GenAI data preparation, including:

• Text processing

• Tokenization

• Chunking strategies

• Prompt/context formatting

• Experience with unstructured data technologies (OCR, NLP pipelines, computer vision data processing).

• Hands-on experience with Dataiku for automation, workflow orchestration, and AI project management.

• Knowledge of MLOps tooling: MLflow, Delta Lake, experiment tracking, CI/CD for ML.

• Understanding of agentic AI system patterns, such as memory architectures, tool APIs, event-driven workflows, and reasoning chain data requirements.

• Strong analytical mindset, attention to detail, and commitment to high data quality.

• Ability to thrive in a fast-paced, evolving AI environment and collaborate across cross-functional teams.

BENEFITS OF WORKING FOR HONEYWELL

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. For more information visit: click here

The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates.

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• Support end‑to‑end data needs for all AI modalities, including classic ML, GenAI/LLMs, and agentic AI systems.

• Build robust, scalable data pipelines for structured, semi‑structured, and unstructured data, including text, documents, images, audio, video, and logs.

• Develop feature engineering pipelines for classic ML, including feature extraction, transformation, and feature store management.

• Build and optimize GenAI and LLM data pipelines, including embedding generation, vectorization, chunking, metadata extraction, and document enrichment for RAG and context retrieval.

• Develop data ingestion and orchestration workflows that support agentic AI, including memory stores, event-driven pipelines, tool-use data flows, and real-time retrieval services.

• Design and implement advanced data solutions using AWS (S3, Glue, Lambda, EMR, Kinesis), Databricks (Spark, Delta Lake, Vector Search), and Dataiku to enable intelligent systems at scale.

• Implement data governance, quality, lineage, monitoring, and observability to support high-performance, trustworthy AI.

• Partner with data scientists, ML engineers, and AI product teams to deliver datasets for model development, fine‑tuning, evaluation, and production inference.

• Optimize pipelines for latency, cost, reliability, and throughput, ensuring AI systems—from batch ML to real-time agents—have the data they need.