Honeywell Forge is looking for a Data Leader to help productize, scale and shape Honeywell Forge Data Platform, that is the backbone of driving AI and Autonomous solutions. This leader will enable data strategy and data execution to enable revenue streams from AI products, drive AI-driven insights for customers and build a trusted scalable data platform that can be democratized for Building and Industrial Automation business. The ideal leader is equal parts strategist and hands-on technologist capable of setting vision, shaping data architecture, and diving deep with senior engineers to designs, optimize pipelines, and resolve complex data challenges. The success of this role will be defined by adoption of Honeywell Forge Data Lake, delivery of deep stakeholder engagement, cost efficiency of data platform, accelerated speed of innovation using data, and demonstrable business value from AI-ready data.

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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.

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Honeywell Forge is looking for a Data Leader to help productize, scale and shape Honeywell Forge Data Platform, that is the backbone of driving AI and Autonomous solutions. This leader will enable data strategy and data execution to enable revenue streams from AI products, drive AI-driven insights for customers and build a trusted scalable data platform that can be democratized for Building and Industrial Automation business. The ideal leader is equal parts strategist and hands-on technologist capable of setting vision, shaping data architecture, and diving deep with senior engineers to designs, optimize pipelines, and resolve complex data challenges. The success of this role will be defined by adoption of Honeywell Forge Data Lake, delivery of deep stakeholder engagement, cost efficiency of data platform, accelerated speed of innovation using data, and demonstrable business value from AI-ready data.

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US PERSON REQUIREMENTS:

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

Required Experience

• 10 or more years in data engineering and/or data and analytics, including 5 or more years leading large-scale data engineering and platform teams in complex environments.
• Deep expertise in data architecture and engineering: data modeling (OLTP/OLAP), big data and query engines, lakehouse, data warehousing, MDM, data integration, CDC, and large-scale batch/stream processing.
• Experience delivering data products at scale with embedded governance, metadata/lineage, and continuous DQ; strong background in data contracts and data observability.
• Time series data streaming expertise, event-driven architectures, and change data capture patterns. Proven success designing and operating enterprise cloud-native data platforms on at least one hyperscaler.
• Practical experience enabling AI/ML: feature stores, model-ready datasets, MLOps integration, and privacy-preserving patterns; comfortable partnering with data scientists and ML engineers.
• Executive presence with the ability to translate complex architectures into business value, present to senior leadership/board-level stakeholders, and lead through influence.
• 5 or more years of people leadership, including hiring, performance management, coaching, and org design.
• Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics
  
  

Preferred Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related discipline (STEM preferred).
• Experience in industrial plants, buildings operations, knowledge of domains such operational efficiency, energy savings, predictive maintenance and operational optimization.
• Familiarity with data monetization, secure data sharing, and analytics in hybrid platforms.

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: Benefits at Honeywell

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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• Define and own the Honeywell connected data engineering strategy, reference architecture for AI-ready data, including cloud platform, data-as-a-service, and automation-first delivery model. Develop and communicate the enterprise data strategy and roadmap, ensuring alignment product requirements, and innovating for data as a service.
• Lead architectural decisions for Honeywell Forge Data Lake comprising IT and OT data, CDC, and integration with multiple source systems; handle reuse, performance, cost efficiency, and time-to-market.
• Architect, implement, and operate hybrid and cloud-native data platforms with heavy automation.
• Establish trusted domains focusing on security, governance, and reuse across business lines. Lead the design and delivery of reusable, trusted data as a service with clear SLAs, documentation, versioning, and APIs; enforce data contracts for product requirements.
• Enable secure, governed data sharing and monetization.
• Provide platform services and reusable capabilities for data science and AI: feature store, model-ready curated layers, governed sandboxes, MLOps integration, and model/data lineage.
• Embed data governance within pipelines: lineage capture, data classification, role-based and attribute-based access, fine-grained controls, and consent management.
• Implement data quality by design: thresholding, anomaly detection, reconciliation, and data SLAs enforced in CI/CD and runtime with automated quarantine/retry/escalation.
• Support build-vs-buy decisions, licensing, cloud spend, and vendor relationships. Scale teams and partners globally while building strong relationships with executives, technical teams, vendors, and business partners to understand needs, influence strategy, and promote best practices.
• Oversee platform implementation projects, balancing innovation, cost-effectiveness, and risk management.
• Scale, mentor, and inspire a diverse, high-performing data engineering and architecture team; develop adaptive hiring and resourcing strategies reflecting organizational growth and transformation.
• Ensure compliance with all risk, regulatory, and audit standards, and maintain rigorous internal controls.