Senior Solutions Engineer

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. HiveMQ's Vision for this role The Solution Advisory team is HiveMQ's strategic technical front line. We are not just answering RFP questions or giving demos, we co-own the deal with our Account Executives, lead with business outcomes, and help our customers turn MQTT and the HiveMQ platform into measurable industrial and AI value. As a Senior Solution Advisor, you will lead our most complex, strategic engagements across EMEA or the US. You will pair with 2 to 3 Account Executives on enterprise opportunities (typically influenced ARR target of $1.5M+), shape buying criteria before competitors do, and act as the trusted advisor that customer champions and executives call before they make a decision. You will also be a multiplier for the team: contributing to our shared playbooks (discovery, value engineering, competitive positioning), and feeding field insights into Product and Engineering. What you will do Own the deal alongside the AE Co-own opportunities from first call to close, with full accountability for the technical and business win Run structured discovery using MEDDPICC: identify economic buyer, decision criteria, decision process, and quantifiable pain Maintain deal hygiene in Salesforce and the Opportunity Qualifier so forecasts are honest and free of surprises Lead with business outcomes (value selling) Translate HiveMQ's technical capabilities into measurable customer outcomes (OEE, time-to-value, cost reduction, risk mitigation, AI readiness) Build customer-specific ROI and TCO models, and use them to justify enterprise deals to economic buyers Proactively shape evaluation criteria so the customer measures what HiveMQ wins on, not what competitors want to be measured on Engage executives and shape strategy Deliver C-suite and VP-level narratives with confidence and gravitas, lead Executive Briefing Center sessions and strategic workshops Advise customers on multi-year transformation roadmaps spanning Unified Namespace, OT/IT convergence, edge connectivity, and AI-enabled manufacturing Influence customer architecture decisions across multi-vendor ecosystems (cloud hyperscalers, streaming platforms, historians, MES/ERP) Architect, advise, and challenge Build reference architectures, do not just present them. Whiteboard under pressure and produce designs that survive contact with a real plant network Lead co-design sessions with customers across edge, broker, data, and cloud layers; cover both current state and target state Advise on the right patterns for resilience, scalability, security, and observability, grounded in HiveMQ's reference architectures and real customer deployments Constructively challenge customer assumptions and design choices when they put the project at risk, offer better alternatives backed by experience and data, not by deference to the loudest voice in the room Conduct architecture reviews of existing customer environments, surface technical risks early, and recommend mitigations before they become production incidents Bridge OT and IT design conversations: translate plant-floor constraints into IT-grade architectures and vice versa, so both sides commit to the same blueprint Run technically rigorous POVs Scope Proofs of Value with co-defined success criteria (technical and business), clear timelines, milestones, and a defined path to commercial decision Lead POV execution end to end: own the test plan, drive a consistent engagement cadence with the customer, and remove blockers as they appear Close out each POV with a clear go / no-go outcome and a clean handover to the Customer Value Manager at signature Position HiveMQ competitively Position HiveMQ effectively against MQTT brokers and IoT cloud services (EMQX, AWS IoT Core, Azure IoT Hub, Mosquitto) as well as adjacent industrial data platforms (HighByte, Litmus, Cognite, Inductive Automation, and AVEVA-class suites) Maintain depth on competitor moves across both broker and industrial-software categories, and feed competitive intelligence back into the team's playbooks Handle tough technical and commercial objections with confidence, including comparisons that go beyond pure broker capability into UNS, contextualization, and industrial DataOps narratives Coach the team and create leverage Mentor Solution Advisors, shadow their calls, and run feedback circles Contribute to team IP: reference architectures, vertical one-pagers, value engineering toolkits, demo environments, and battle cards Represent HiveMQ externally through blogs, webinars, and regional conference speaking Who you are We hire Solution Advisors who already understand operational environments. Industrial fluency is a prerequisite, not a stretch goal. Platform, cloud, and AI knowledge are teachable on the job, OT instincts are not. A strong candidate can: Talk specifically about industrial environments without prompting: names plants, asset classes, protocols, vendors, and project failure modes, and does not speak in slideware abstractions Describe a real deployment that went sideways and explain why, ideally including the organizational root cause and not just the technical one Articulate why "move fast and break things" does not translate to OT, because the "things" are physical processes with real consequences Distinguish IT buyers from OT buyers and explain how those buying centers behave differently Hold a credible conversation with a plant engineer, controls engineer, or operations leader without falling back to vendor-marketing language Build strong, long-term relationships with both technical and executive stakeholders, and level-shift between a CTO conversation about industrial AI and a deep-dive whiteboard on broker clustering in the same call Push back when needed: challenge customer architecture and design decisions with evidence and alternatives, not deference Believe value selling drives bigger, faster deals, and can prove it with examples Thrive in a fast-paced, high-ownership, highly collaborative environment, and bring others up with you The background we require This is the floor. Candidates who do not meet it are not a fit, regardless of seniority or polish elsewhere. You must have done at least one of the following: Worked inside a manufacturer, energy company, utility, logistics operator, or similar industrial business in a technical or technical-adjacent role (data, automation, controls, OT engineering, plant IT, MES, historian, SCADA) Worked at a system integrator delivering OT projects on customer sites Worked at an industrial ISV or vendor whose product lives in plant or field environments (e.g. Inductive Automation, Litmus, HighByte, PTC / Kepware / ThingWorx / Velotic, Tulip, Cognite, AVEVA, GE Digital / Proficy, Siemens Industrial Edge, Rockwell FactoryTalk) The test is firsthand exposure to brownfield realities: mixed-vintage equipment, uptime constraints, plant-floor politics, change windows, safety reviews, and the gap between what a tag says and what the asset is actually doing. What does not count: Pure IT, cloud, or SaaS background with no plant-floor or field exposure "Industry" experience that is actually corporate IT at an industrial company (e.g. SAP rollouts, M365 migrations, IT helpdesk for a manufacturer), the work that matters happens below the IT / OT boundary Heavy reliance on AI, agent, or generic-platform talking points to compensate for thin domain depth What we are looking for The ideal candidate has most of the following. Advisory and commercial depth (Value Selling) 5+ years in software pre-sales, solution engineering, or solution architecture, including time in a senior or lead capacity on enterprise deals Demonstrated success owning or co-owning enterprise opportunities of $500K+ ACV, with documented influence on $1.5M+ in annual ARR Hands-on use of MEDDIC / MEDDPICC, structured discovery, and outcome-based qualification Proven ability to build and present ROI / TCO business cases to economic buyers Strong executive presence: comfortable presenting to C-level and VP audiences, handling tough questions, and navigating internal customer politics Experience positioning against MQTT brokers and IoT cloud services (EMQX, AWS IoT Core, Azure IoT Hub, Mosquitto) and adjacent industrial data platforms (HighByte, Litmus, Cognite, Inductive Automation, AVEVA-class), and leading competitive bake-offs across both categories Technical depth Strong working knowledge of MQTT (3.1.1 and 5), including QoS semantics, session handling, retained messages, shared subscriptions, and topic design Good understanding of the HiveMQ Platform: broker architecture, clustering, Enterprise Security Extension (TLS, AuthN/AuthZ, realms), Bridge and Kafka extensions, Data Hub policies and transformations, Edge protocol adapters, Control Center Comfortable with Kubernetes for production workloads (StatefulSets, Helm, the HiveMQ Operator, monitoring with Prometheus / Grafana) Familiar with at least one major cloud (AWS, Azure, or GCP) and with networking concepts (TCP/IP, WebSockets, PrivateLink / Private Endpoint, load balancing) Solid grounding in enterprise security, reliability, interoperability, and observability Working knowledge of databases relevant to IoT data flows (PostgreSQL, MySQL, MS SQL) and of streaming patterns Development and automation skills Comfortable reading and writing code to prototype integrations, build custom demos, or unblock customer POVs without waiting for engineering Hands-on with at least one of Java/Kotlin, Python, JavaScript/Node.js Familiar with containerization (Docker) and Infrastructure as Code (Terraform, Ansible, Helm) Comfortable with scripting (Bash, PowerShell), REST APIs, and basic CI/CD concepts Should be able to write or extend HiveMQ extensions, Edge protocol adapters, or Data Hub policies/transformations to address specific customer requirements after an initial training Industry and domain expertise Has stood up real systems in industrial environments, not just demoed software; bonus if you have done implementation work at a system integrator or industrial ISV before moving into pre-sales Comfortable across the IT / OT boundary: can talk to a controls engineer about PLCs and a cloud architect about Kubernetes in the same call Working knowledge of at least one major industrial protocol (MQTT, OPC UA, or Modbus). You do not need to be expert in all of them, but you do need to know the world they live in Deep knowledge in at least 2 of: Smart Manufacturing / IIoT, Connected Vehicles & Mobility, Energy & Utilities, Logistics, with experience weighted toward Discrete Manufacturing, Pharma, and Process industries (our current ICP focus) Familiarity with industrial standards and protocols relevant to your verticals: ISA-95, Unified Namespace (UNS), Sparkplug B, OPC UA, MODBUS TCP, VDA5050, and others such as Siemens S7, ADS Beckhoff, MTConnect Understands why a clean cloud-native architecture often does not survive contact with a real plant network, and knows what good integration patterns look like in brownfield environments Awareness of OT/IT convergence patterns and the role of MQTT in industrial AI / data platforms Soft skills and ways of working We weigh soft skills as heavily as the technical buckets above. A strong Senior Solution Advisor combines the following, consistently and without prompting: Discovery as a discipline, not a checklist: asks layered, business-anchored questions, listens for what is not being said, and reframes customer pain into quantifiable outcomes before reaching for a demo Executive presence and level-shifting: moves credibly between a CxO conversation about industrial AI strategy and a whiteboard session on broker clustering or UNS modeling within the same engagement, adjusting depth and language to the room Constructive pushback: challenges customer assumptions, AE expectations, and internal product positioning when the evidence supports it, and does so with data and alternatives rather than friction Follow-through and operational rigor: owns commitments end to end, closes loops with customers and internal stakeholders, keeps Salesforce and the Opportunity Qualifier honest, and is the person teammates trust to land what they said they would land Self-awareness and coachability: knows where their depth ends, asks for help early, debriefs wins and losses with intellectual honesty, and applies feedback visibly in the next engagement Collaboration and multiplier behavior: lifts peers and junior Solution Advisors, contributes to shared IP (playbooks, reference architectures, battle cards), and treats team success as part of personal success Collaboration and influence Strong written and verbal communication; able to present at customer meetings, webinars, and conferences Experience mentoring or coaching less senior pre-sales colleagues Comfortable contributing to internal thought leadership: playbooks, reference architectures, content Logistics Located in EMEA (preferably UK, Germany, Austria, or France) or in the United States Willing to travel up to 25% for customer engagements, partner sessions, and conferences Bonus points Multilingual (English plus French, German, or Spanish) Hands-on experience with industrial AI, agentic workflows, ontologies, or DataOps in a manufacturing or energy context Prior experience as an Executive Sponsor for strategic enterprise accounts Published blog posts, whitepapers, or conference talks in the IoT / industrial space How we will measure success Influenced ARR (target $1.5M+ per year) and contribution to new logo acquisition Win rate on engaged opportunities, and compression of demo to POV to signature cycle time Accuracy of POV scoping (deals close without surprise re-pricing) and Opportunity Qualifier (MEDDPICC) completeness Number of executive engagements led, reference customers developed, and team IP / thought leadership produced Mentorship impact: progression of Solution Advisors you coach To learn more about HiveMQ, a good place to start is our customer case studies: https://www.hivemq.com/customers/ EXCERPT FROM OUR CUSTOMER LIST https://www.hivemq.com/customers/ Informations about our job advertisements Job advertisements of HiveMQ GmbH are always directed at female, male and various applicants, regardless of age, gender, religion, sexual identity, disability, race, ethnic origin, world view, etc. The selection of a candidate is exclusively based on qualifications. For organisational reasons, we cannot return application documents and cannot reimburse any expenses that you incur during the application process.