Company Description
QAD is a leading provider of ERP solutions purpose-built for manufacturing industries — automotive, life sciences, food & beverage, high tech, and industrial. Serving thousands of global manufacturers, QAD's Adaptive Manufacturing Cloud helps companies operate with greater precision, agility, and intelligence.
Enterprise software is entering a third era. The first gave manufacturers a System of Record — ERP that answered 'what do we have and what did we commit to?' The second gave them Data Infrastructure — the ability to move, analyse, and query that data at scale. The third era is domain-specific intelligence: AI agents that can act autonomously on manufacturing data, but only if that data has been given the context, relationships, and governed rules that allow an agent to reason correctly.
ERA — QAD's Enterprise Resource Allocation platform — is the domain intelligence layer that sits between any ERP and any AI agent. It encodes what manufacturing data means, governs what agents are permitted to do, and makes every autonomous decision traceable and accountable.
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
Represent the Data, AI & Analytics platform in senior leadership forums, including roadmap reviews with the President's office
Qualifications
Experience
15+ years in product management, with significant time in data platforms, analytics, or AI/ML products in enterprise SaaS environments
Proven track record owning 0-to-1 and scale-up AI Insights or analytics platforms — from early access through enterprise GA
Experience in ERP, CRM, or broader B2B enterprise software; deep familiarity with the data needs of complex enterprise operations
Demonstrated ability to drive measurable outcomes: user growth, CSAT improvement, cost optimization, and revenue expansion through platform capabilities
Prior experience working directly with Sales, Pre-Sales, and Marketing to build GTM motions and enablement for platform or data products — not just handing off a roadmap but actively participating in market positioning and deals
History of structured customer discovery practice — conducting interviews, running advisory boards, or embedding with customers to build evidence-based roadmaps
Technical Depth
Hands-on fluency with: LLM evaluation frameworks, semantic layer design, knowledge graphs, RAG architectures, conversational analytics, and agentic AI
Strong command of data platform architecture: Snowflake, Databricks, delta sharing, ETL/ELT, event-driven pipelines, federated search
Ability to engage deeply with engineering on infrastructure trade-offs — pipeline cost, performance at scale, and AI feature architecture
Working knowledge of SQL, data modelling, and API design sufficient to hold authoritative technical conversations
Leadership & Communication
Director-level leadership maturity: translates business strategy into functional plans, reconciles multi-stakeholder views, and drives execution across matrixed organizations
Comfortable operating on complex, ambiguous problems where established frameworks don't apply — defines the approach, not just follows one
Makes decisions with long-horizon awareness; understands that platform architecture choices compound over years and plans accordingly
Proven experience building and scaling product teams; consistent track record of developing strong PMs and fostering a culture of ownership
Excellent executive communication — makes complex platform decisions accessible to non-technical senior leaders and enterprise customers alike
Actively engages with major customers and cross-functional leaders; able to negotiate priorities and build consensus across competing stakeholder views
Strong collaborator with UX and Design — understands that intelligence platform adoption lives or dies on usability, not just capability
Preferred Background
Prior experience at enterprise SaaS companies scaling AI/analytics platforms to multi-thousand customer deployments
Familiarity with manufacturing ERP data models, production operations, or supply chain analytics is a strong plus
Experience defining and owning monetization strategy for data or AI platform features
Patents, publications, or open-source contributions in AI, data infrastructure, or analytics are valued
Why This Role at QAD
Direct impact: Your roadmap decisions will shape AI capabilities used by thousands of manufacturers worldwide
Strategic mandate: This is not a maintenance role — it is a build mandate at the intersection of AI, data, and enterprise ERP
Executive visibility: You will operate at the centre of QAD's platform transformation, with direct access to the President's office and cross-functional leadership
Pune platform hub: QAD is actively expanding its platform engineering and product capabilities in India — you will be a founding voice in that growth
Additional Information
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Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work-life balance.
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Opportunity to join a growing business, launching into its next phase of expansion and transformation.
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Collaborative culture of smart and hard-working people who support one another to get the job done.
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An atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.
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Compensation packages based on experience and desired skill set
About QAD:
QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.
QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
