Honeywell

Job Description

KEY RESPONSIBILITIES

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

US PERSON REQUREMENTS:

Due to compliance with U.S. expert control laws and regulations, candidate must be a U.S. Person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

YOU MUST HAVE

• Minimum 8 years of hands-on experience in building data pipelines using large-scale distributed data processing tools, frameworks & platforms (Python, Spark, Databricks)
• 6+ years of extensive experience in data management concepts, including data modeling, CDC, ETL/ELT processes, data lakes, and data governance.
• 4+ years of hands-on experience with PySpark/Scala
• 4+ years of experience with cloud platforms (Azure/GCP/Databricks) particularly in implementing AI/ML solutions

WE VALUE

• Experience implementing RAG architectures and working with LLM-powered applications
• Expertise in real-time data processing frameworks (Kafka, 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 with graph databases and knowledge graphs for AI applications
• Experience working with distributed teams and cross-functional collaboration
• Knowledge of data security and governance practices for AI systems
• Expertise in version control systems, CI/CD methodologies
• Experience working on analytics projects with Agile and Scrum Methodologies

About Us

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.