Title: Director of Product Management, Data Foundation & Ontology

About the Company

We are a leading technology platform for intelligent orchestration of complex physical systems—spanning energy, infrastructure, operations, and the built environment. Our real-time intelligence layer connects enterprise, operational, and IoT data across siloed systems, sensors, and edge environments, enabling monitoring, optimisation, automation, and AI-driven decision-making at scale. We serve thousands of global customers across industries such as utilities, manufacturing, transportation, buildings, and logistics, helping them improve performance, resilience, resource efficiency, and sustainability.

Job Summary

We are seeking a senior product leader to own the engineering organisation responsible for the trusted data, integration, and semantic foundation of our platform. This role will build and lead the teams that unify fragmented IT, OT, and IoT data, transform heterogeneous information into reliable operational context, and make that context safely consumable by analytics, optimisation applications, and AI agents.

This is an end-to-end leadership position with accountability for technical strategy, architecture, organisation design, talent acquisition, delivery, and operational excellence. The successful candidate will build a high-performing organisation spanning data platform, industrial integration, ontology and semantic systems, developer experience, security, governance, and reliability. They will remain close to architecture and execution while establishing the standards and operating model needed to scale globally.

Key Responsibilities

Engineering & Platform Leadership

Define the product vision, target architecture, and multi-year technical roadmap for the data foundation and ontology layer.
Own end-to-end delivery of the platform—including connectors, ingestion, streaming, change data capture, time-series processing, canonical models, metadata, catalog, lineage, data quality, ontology services, APIs, SDKs, and governed actions.
Make sound architectural decisions across distributed systems, storage, compute, graph and semantic technologies, event-driven services, multi-tenancy, cloud, hybrid, and edge deployments.
Establish clear platform boundaries, service contracts, extension points, and reference patterns to ensure customer deployments strengthen the core product rather than create one-off implementations.
Partner with engineering, AI, field-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/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 (e.g., OPC UA, MQTT, Modbus) while maintaining a vendor-neutral architecture.
Design for intermittent connectivity, edge processing, high-volume telemetry, late/out-of-order data, schema drift, source-system changes, and resilient synchronisation across cloud and on-premise environments.
Define the build-buy-partner strategy for connectors and integration tooling, balancing breadth, reliability, 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/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 compromising 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 & Organisation Leadership

Design the engineering organisation, team topology, leadership structure, hiring plan, and capability roadmap.
Recruit, lead, and develop engineering managers, architects, platform engineers, data/integration engineers, ontology/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, robust 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, data 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 & Cross-Functional Leadership

Engage directly with enterprise customers, operational leaders, IT/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, field-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 & Experience

Required

10+ years of experience in software engineering, data engineering, or distributed platform development, including at least 5 years leading engineering teams and managers.
Proven track record of building and scaling a technically complex enterprise data or platform product from ambiguous/early-stage requirements into secure, reliable production use.
Demonstrated experience recruiting, organising, 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 modelling, 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 modelling, 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 (e.g., historians, SCADA, MES, EAM/CMMS, 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/agent governance, tool permissioning, human-in-the-loop workflows, or governed write-back.
Experience establishing developer platforms, APIs, SDKs, connector ecosystems, or open-source/partner programs.
Experience leading globally distributed teams and delivering platforms across multiple regulatory and deployment environments.