Director of Product Management, Data Foundation & Ontology

About Univers

Univers is the Platform for Physical AI, helping organizations orchestrate complex energy, infrastructure and operational systems with intelligence that understands, predicts and acts in the physical world.

Our AIP platform connects IT, OT and IoT data across enterprise systems, assets, sensors and edge environments, creating a real-time operational foundation for monitoring, optimization, automation and AI-driven action.

Across energy, utilities, buildings, manufacturing, transportation and logistics, Univers helps organizations improve performance, resilience, resource efficiency and sustainability. With more than 400 million connected sensors and devices, more than 1,005 GW of energy assets under AI management and more than 800 customers globally, Univers operates one of the world’s largest real-time intelligence platforms for physical infrastructure.

For more information, please visit

https://univers.com/

Job Summary

The Director of Product Management, Data Foundation & Ontology will build and lead the engineering organization responsible for the trusted data, integration and semantic foundation of the Univers AI Platform. The platform will connect fragmented IT, OT and IoT environments, transform heterogeneous data into reliable operational context, and make that context safely usable by analytics, optimization applications and AI agents.

This is a senior product leadership role with end-to-end accountability for technical strategy, architecture, organization design, hiring, delivery and operational excellence. The successful candidate will build a high-performing team spanning data platform, industrial integration, ontology and semantic systems, developer experience, security, governance and reliability. They will remain close to architecture and execution while setting the standards and operating model required to scale the platform globally.

Key Responsibilities:

Engineering and Platform Leadership

• Define the product vision, target architecture and multi-year technical roadmap for the data foundation and ontology layer of the Univers AI Platform.
• Own delivery of the end-to-end platform, including connectors, ingestion, streaming and change data capture, time-series processing, canonical models, metadata, catalog, lineage, data quality, ontology services, APIs, SDKs and governed actions.
• Make sound architecture decisions across distributed systems, storage, compute, graph and semantic technologies, event-driven services, multi-tenancy, cloud, hybrid and edge deployment.
• Establish clear platform boundaries, service contracts, extension points and reference patterns so customer deployments strengthen the core product rather than create one-off implementations.
• Partner with Engineering, AI, Forward Deployed Engineering and domain leaders to translate customer and industry needs into a sequenced engineering plan. IT, OT and IoT Data Integration
• Build a scalable connectivity framework for enterprise applications, databases, data lakes and warehouses, APIs, files, event buses and document repositories.
• Lead integration with operational and industrial systems including historians, SCADA, MES, BMS/EMS, gateways, controllers and sensor platforms.
• Guide support for relevant protocols and standards such as OPC UA, MQTT and Modbus while maintaining a vendor-neutral platform architecture.
• Design for intermittent connectivity, edge processing, high-volume telemetry, late and out-of order data, schema drift, source-system change and resilient synchronization across cloud and on-premise environments.
• Define the build, buy and partner strategy for connectors and integration tooling, balancing breadth, reliability, implementation speed and long-term maintainability. Ontology, Context and AI Readiness
• Lead the design of an operational ontology that represents real-world objects, properties, relationships, events, time series, documents, business logic and governed actions.
• Establish engineering capabilities for semantic mapping, entity resolution, master and reference data, asset hierarchies, temporal and geospatial context, schema evolution, versioning, validation, search and discovery.
• Create a permission-aware context layer that supports analytics, machine learning, retrieval augmented generation and agentic applications without bypassing enterprise security or governance.
• Enable governed actions and write-back to operational systems with policy enforcement, approval controls, auditability, idempotency and safe failure handling.
• Set pragmatic standards for knowledge graphs and semantic technologies, using approaches such as RDF, OWL, SHACL or property graphs where they provide clear product and engineering value.

People and Organization Leadership

• Design the engineering organization, team topology, leadership structure, hiring plan and capability roadmap required to build and operate the platform.
• Recruit, lead and develop engineering managers, architects, platform engineers, data and integration engineers, ontology and semantic engineers, developer-experience engineers and site reliability engineers.
• Set clear expectations for ownership, decision-making, technical quality, delivery and operational accountability; manage performance and create strong career-development paths.
• Build an inclusive, high-trust and high-accountability culture that combines customer urgency with platform discipline, strong documentation, design reviews and engineering excellence.
• Coach leaders and senior engineers while staying sufficiently hands-on to review architecture, interfaces, data models, security controls and critical implementation choices.

Delivery, Reliability and Governance

• Own execution from technical discovery and design through development, testing, release, adoption and production operation.
• Establish engineering planning, architecture governance, software-development lifecycle, quality standards and release practices appropriate for an enterprise platform.
• Define and operate service-level objectives for availability, latency, freshness, correctness, scalability and recovery; build mature observability, incident-management and continuous improvement practices.
• Make security, tenant isolation, identity, policy enforcement, privacy, residency, lineage and audit native capabilities of the platform.
• Manage technical risk, dependencies, budgets and capacity while communicating progress and trade-offs clearly to executive stakeholders.

Customer and Cross-functional Leadership

• Engage directly with enterprise customers, operational leaders, IT and data teams, security stakeholders and field engineers to understand real deployment constraints and recurring platform needs.
• Use selected deployments as design partnerships, turning field learning into reusable platform capabilities and measurable reductions in implementation time.
• Partner with Engineering Forward Deployed Engineering, Value Engineering and commercial teams on reference architectures, demonstrations, enablement and strategic opportunities.
• Represent the platform with senior customers and partners, explaining complex technical choices in clear business and operational terms.

Qualifications and Experience Required

• 10 or more years of experience in software engineering, data engineering or distributed platform development, including at least five years leading engineering teams and managers.
• A proven record of building and scaling a technically complex enterprise data or platform product from ambiguous or early-stage requirements into secure, reliable production use.
• Demonstrated experience recruiting, organizing and developing high-performing engineering teams, with direct accountability for delivery, performance and career growth.
• Deep expertise in modern data platforms, including batch and streaming ingestion, event driven architecture, APIs, data modeling, metadata and catalogs, lineage, quality, governance and access control.
• Experience integrating heterogeneous enterprise systems and working with both IT data and OT/IoT data from industrial or physical environments.
• Strong understanding of semantic modeling, knowledge graphs or ontology-driven systems and how objects, relationships, events, logic and actions support operational workflows.
• Strong architecture and software-engineering judgment across distributed systems, cloud platforms, data stores, interfaces, reliability, security, performance and cost.
• Experience delivering multi-tenant SaaS, hybrid-cloud, edge or other enterprise software under demanding security and reliability requirements.
• Experience working directly with large enterprise customers and translating fragmented workflows, data constraints and operational realities into reusable technical capabilities.
• Excellent written and verbal communication, with the ability to align executives, engineers, product teams, field teams and customers around difficult technical decisions.
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering or a related technical field, or equivalent practical experience.

Preferred

• Experience in energy, utilities, manufacturing, transportation, buildings, infrastructure or another asset-intensive industry.
• Hands-on familiarity with industrial systems such as historians, SCADA, MES, EAM/CMMS or BMS/EMS and protocols such as OPC UA, MQTT or Modbus.
• Experience with data catalogs, semantic layers, digital twins, master data management, entity resolution or ontology tooling.
• Experience with agentic AI, RAG, model and agent governance, tool permissioning, human-in the-loop workflows or governed write-back.
• Experience establishing developer platforms, APIs, SDKs, connector ecosystems or open-source and partner programs.
• Experience leading globally distributed teams and delivering platforms across multiple regulatory and deployment environments.