Director, Software Architect

Role Overview

• 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.

Requirements

• 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

Tech Stack

• Cloud

Benefits

• employer subsidized Medical, Dental, Vision, and Life Insurance
• Short-Term and Long-Term Disability
• 401(k) match
• Flexible Spending Accounts
• Health Savings Accounts
• EAP
• Educational Assistance
• Parental Leave
• Paid Time Off (for vacation, personal business, sick time, and parental leave)
• 12 Paid Holidays