Director of Product Management - Data Intelligence

HiveMQ is the Industrial AI Platform helping enterprises move from connected devices to intelligent operations. Built on the MQTT standard and a distributed edge-to-cloud architecture, HiveMQ connects and governs industrial data in real time, enabling global leaders like Audi, BMW, Eli Lilly, and Siemens to operationalize AI and drive innovation at scale.

At HiveMQ, our culture is Effortless → Empowered → Relentless. We make the complex simple, act with confidence and ownership, and never stop pushing the boundaries of what’s possible. Join us to power the future of intelligent industry.

What You'll Do

Strategic Leadership & Vision

• Define and drive product strategy for the Data Intelligence portfolio, focusing on context/governance, data modeling for decision-making, real-time insights, and AI-driven actions
• Lead 0-to-1 product initiatives from conception through market success, leveraging your proven experience launching new data intelligence and analytics products
• Bridge the gap between raw data streams and intelligent action—envision how customers will model, contextualize, and govern their data to unlock business value
• Champion AI enablement: drive product capabilities that make IoT data AI-ready for predictive analytics, automated decision-making, and intelligent actions
• Establish clear, inspiring vision for your teams derived from company strategy, helping prioritize and reprioritize for organizational success
• Think cross-functionally and ahead: understand how data intelligence connects to GTM, our revenue model, and anticipate the evolution of data governance, context management, and AI-driven automation

Business Impact & Accountability

• Drive measurable business outcomes across adoption, usage, revenue, and customer satisfaction metrics for your product portfolio
• Own the data story: work with product data analysts to instrument products effectively, measure impact, and report progress toward business objectives
• Communicate value effectively through data-backed storytelling to executives, customers, and cross-functional stakeholders
• Identify and address organizational bottlenecks, proposing process improvements as business scale demands
• Make hard calls: determine when products or features should be terminated if they don't deliver desired outcomes

Cross-Functional Collaboration

• Partner deeply with R&D leadership (Team Leads, Directors) to ensure technical feasibility and delivery excellence
• Collaborate with GTM organizations including Sales, Customer Success/Support, Alliances, and Product Marketing at director and VP levels
• Manage strategic customer relationships and handle escalations with appropriate oversight
• Build consensus across departments on complex, cross-functional initiatives
• Develop & present roadmaps and proposals to senior leadership, occasionally engaging with C-suite executives

Customer-Centricity

• Maintain intimate customer knowledge through ongoing strategic customer relationships
• Advocate for customer needs by framing capabilities in terms of customer outcomes and business priorities
• Validate product-market fit through continuous discovery and customer feedback loops
• Drive customer intimacy beyond strategic accounts to understand broader market needs

 

What You Bring

Required Experience & Track Record

• 8-15 years total work experience, with significant portion in product management or consulting
• 4+ years of people management experience, including leading distributed teams across geographies and time zones
• Proven 0-to-1 success: At least two successful new product launches from conception to market success in the data and analytics domain
• Data-driven product development: demonstrated success using quantitative and qualitative methods to drive product decisions
• Budget and resource management: experience allocating resources across teams and managing to budget

Domain Expertise (Highly Valued)

• Data intelligence and contextualization: understanding of how to add semantic meaning, business context, and governance to streaming data
• Data modeling for decision-making: experience building products that enable customers to model data in ways that drive automated decisions and actions
• Real-time analytics and insights: proven track record with streaming analytics, complex event processing, or real-time intelligence platforms
• AI/ML integration: familiarity with how data preparation, feature engineering, and data quality impact AI model effectiveness and AI-dri