Director of Product, Platform

Job Description

This role owns a key pillar of ERA — the Manufacturing Data Fabric & Intelligence layer. This is the foundational intelligence core: the semantic layer that encodes what manufacturing data means, the metric registry that governs how business outcomes are measured, and the AI Insights and conversational analytics capabilities that surface intelligence to users and agents alike. Without this layer, the rest of ERA cannot function. It is the prerequisite for every downstream agent action, every governed decision, and every autonomous workflow ERA enables.

As Director of Product for Data, AI, Reporting & Analytics, you will report directly to the Head of Platform Product and lead a team of Product Managers and Technical Product Owners. Your scope is the intelligence core — not the integration layer, not the governance engine, not the developer API. You will partner closely with the Directors leading those pillars, but your mandate is singular: make manufacturing data meaningful, queryable, and AI-ready at enterprise scale.

This is a high-visibility, high-leverage role. The semantic layer is ERA's primary moat — the hardest layer to build and the one no horizontal AI platform will invest in replicating. Your roadmap decisions will compound over years and directly determine QAD's competitive positioning in the era of autonomous manufacturing.

The Opportunity

The foundation exists — the mandate now is to build ERA's intelligence core into the definitive manufacturing ontology platform. You will have the scope to:

• Define and own the manufacturing semantic layer — encoding supplier criticality, lead time patterns, quality thresholds, and operational constraints in a form AI agents can reason from

• Build the contextual intelligence layer that passes metric definitions, data distributions, and business rules to LLMs for accurate, anomaly-aware narrative generation

• Drive QAD's conversational analytics strategy — enabling non-technical manufacturing users to query operations in natural language without SQL or BI expertise

• Establish the AI evaluation framework that governs quality, accuracy, and latency of every AI-generated insight shipped to enterprise customers

• Evolve the platform from reactive reporting to proactive, agentic intelligence — where the system surfaces recommended actions, not just data

Key Responsibilities

Platform Strategy & Roadmap

• Own the multi-year product roadmap for ERA's Manufacturing Data Fabric & Intelligence layer — from raw data pipeline to user-facing AI insights

• Define the manufacturing semantic layer: encoding metric definitions, operational constraints, supplier relationships, and business rules into a governed ontology that AI agents can reason from

• Translate QAD's ERA vision into concrete, sequenced product bets for the intelligence core — partnering with Directors leading the Integration, Governance, and API pillars to ensure the full platform coheres

• Lead the evolution of AI Insights capabilities: contextual anomaly detection, plain-language narrative generation, and agentic root cause analysis

AI & Conversational Analytics

• Drive the conversational analytics product — ontology design, knowledge graph, context graph, and intent detection for natural language querying

• Establish and own the AI evaluation framework: latency, accuracy, relevancy gates, LLM-as-judge, and human-in-the-loop oversight

• Architect the contextual layer that passes metadata, data distributions, and business rules to LLMs for accurate, enterprise-grade output

• Define QAD's approach to agentic AI — moving from reactive Q&A to proactive, action-triggering intelligence within ERP workflows

Enterprise Self-Service Reporting

• Own the product strategy for self-service report building — enabling business users across manufacturing, finance, supply chain, and operations to create, customise, and share reports without engineering dependency

• Define the RBAC model for reporting: row-level and column-level access controls, report sharing permissions, data scoping by plant, region, and business unit — ensuring enterprise customers can safely deploy self-service capabilities across large, complex org structures

• Lead the go-to-market and rollout strategy for self-service reporting across B2B industry verticals — working with Customer Success and Solutions Engineering to drive adoption, define onboarding playbooks, and reduce time-to-value for enterprise deployments

• Set the product bar for enterprise-grade report authoring: scheduling, export, embedding, white-labelling, and audit trails that meet the compliance and operational needs of regulated industries

• Establish feedback loops with enterprise customers to continuously refine the self-service experience — tracking adoption metrics, identifying capability gaps, and prioritising the roadmap against real user workflows

Data Infrastructure & Intelligence

• Partner with Engineering to define the data lake, pipeline, and event architecture that underpins ERA's intelligence layer — optimising for AI-readiness, scale, and cost efficiency

• Own the metric registry and semantic data model — ensuring consistent, governed definitions of manufacturing KPIs across all ERA consumers

• Collaborate with the Governance & Compliance pillar to ensure intelligence outputs meet enterprise compliance requirements; own the data quality and freshness standards within your layer

• Define monetization strategy for intelligence platform capabilities including governed data exports, delta sharing, and partner integrations

GTM, Sales & Market Enablement

• Act as the product authority for Sales and Marketing on ERA's Data, AI, and Analytics capabilities — providing positioning, messaging, and competitive differentiation across the full intelligence layer, not just reporting

• Partner with GTM teams to develop selling motions for AI Insights, conversational analytics, and self-service reporting — translating platform capabilities into clear business value narratives for manufacturing buyers

• Build and maintain sales enablement assets: demo environments, capability decks, objection-handling guides, and ROI frameworks that equip field teams to position the intelligence layer confidently

• Engage with Marketing on thought leadership, analyst relations, and campaign strategy — ensuring QAD's AI and analytics narrative reflects the depth and differentiation of what the platform can actually deliver

• Work with Solutions Engineering and Pre-Sales on complex enterprise deals — providing product depth in discovery, RFP responses, and proof-of-concept engagements where the intelligence layer is a key differentiator

Customer Discovery & UX Partnership

• Maintain a structured and continuous customer discovery programme — conducting regular interviews, site visits, and advisory sessions with manufacturing customers across segments and geographies

• Translate customer discovery into sharp problem definitions and validated hypotheses before committing roadmap resources — ensuring the team builds what the market needs, not what it assumes

• Serve as a named executive contact for key enterprise customers and design partners; build relationships that provide early access to real workflows, pain points, and adoption barriers

• Partner closely with UX and Design from problem framing through to launch — ensuring the intelligence layer is not just technically capable but genuinely usable by non-technical manufacturing users

• Champion the user in internal prioritisation debates; bring customer evidence — not opinion — to roadmap and trade-off discussions

Leadership & Stakeholder Management

• Lead and grow a team of PMs and TPOs; create a high-performance, execution-focused product culture with strong discovery and delivery discipline

• Drive alignment across Product, Engineering, UX, Customer Success, Sales, and Executive leadership on platform priorities and sequencing

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