About Us

Cumulocity is the preferred choice for industrial equipment makers and is trusted by major brands to power smart connected products in manufacturing, green energy, fleet management, consumer electronics and more. With roots dating back to 2012, Cumulocity is now one of the leading Industrial IoT platforms in the market, connecting devices, making them intelligent with AI and providing advanced analytics capabilities.

Cumulocity serves more than 200 customers in over 20 countries with around 300 employees. Following an investor-backed Management Buy-Out in January 2025, we are an independent scale-up on a high-growth trajectory, building internal corporate functions that support our customers, employees and investors.

Position Overview

We are looking for a Product Manager to own Cumulocity's Data Operations capabilities: Everything it takes to turn raw machine data into trusted, AI-ready data.

Our customers connect machines and systems that speak dozens of protocols, from fieldbus protocols such as Modbus to LPWAN, with binary payloads, proprietary formats and undocumented field names. That data has to be ingested reliably, parsed and enriched with context, analyzed as it streams, and stored so that people, analytics and AI agents can derive insights and drive actions from it.

For years, an entire consulting industry made a living writing this data integration logic by hand. Agentic AI is ending that. Work that used to need a specialist and six weeks is becoming something a customer describes in plain language and an agent assembles. We are building the AI context layer for that world: one connected set of Data Operations capabilities spanning ingestion, harmonization, contextualization, streaming analytics and an open data lake, designed so that people and agents can both operate and use the data from their connected assets.

You will set the Data Operations product strategy and roadmap, and drive the evolution of Data Preparation, Streaming Analytics and DataHub. You will not start alone: experienced Product Managers and engineers onboard and mentor you on these products. The role is hands-on and high-impact, at the intersection of customer needs, market opportunities, technology innovation and commercial growth, with direct access to senior leadership.

This is not a project management or requirements coordination role. We expect you to make product decisions, challenge assumptions, balance competing priorities and take accountability for measurable outcomes.

What You Can Expect

Strategy

• Own the product strategy, roadmap and business case for Data Operations, aligned with company objectives, customer needs and market opportunities.
• Define and track the metrics that matter — adoption, usage, reliability, customer satisfaction and commercial performance — and use them to steer decisions.
• Evangelize practical agentic AI use cases in Industrial IoT, internally and with customers.

Discovery and Delivery

• Run continuous discovery: weekly customer conversations, workshops, prototype validation, competitive analysis and structured feedback from our preview customers.
• Translate customer and market problems into clear product concepts, requirements, user stories and a prioritized backlog.
• Work daily with the Engineering leads and our Design team to shape solutions that balance customer value, technical feasibility, effort and time to market.
• Make transparent trade-offs between strategic initiatives, customer commitments, technical debt and platform work, and explain the reasoning behind them.

Go-To-Market

• Partner with Product Marketing on positioning, value propositions, launch plans and enablement material, and act as a credible technical voice in customer and partner conversations.
• Support Sales, Pre-Sales, Customer Success and Professional Services in enterprise opportunities, and bring what you learn there back into the roadmap.
• Coordinate releases across Engineering, Operations, Marketing, Sales, Customer Success, Professional Services, and Support, and follow adoption after launch to verify the outcome.

Team

• Work as part of the Product Management team: share context and learnings, review and challenge your peers' decisions, and step in where another Product Manager needs support, so that the platform evolves as one coherent product.

Qualifications

You have owned B2B software products. Technical products in platform, infrastructure or developer-facing areas, where the user is an engineer and the buyer is an enterprise. You should bring experience across the full product lifecycle.

You are fluent in agentic AI in practice. You have built with LLMs and agents yourself. You understand tool calling, context and memory, why agents fail in long multi-step tasks, and how to evaluate reliability before putting them in front of customers. You use these tools daily in your own work.

You work with high agency. You have a bias towards action and try to solve problems immediately rather than waiting for an official mandate or explicit permission.

You experiment rather than debate. When the answer is unclear, you design a small, fast experiment instead of arguing from opinion, and you keep what you have measured clearly separated from what you still assume.

You are a team player. You challenge your peers openly, accept challenges in return, and make the other Product Managers better. You see the team's outcome as your own.

You explain hard things simply — to a plant engineer, to a CTO and to a room of skeptical developers.

You work in English. German is useful here, but not required.

What will set you apart

• An industrial or OT background — IoT protocols, edge computing, SCADA or complex event processing.
• Experience with lakehouse or streaming data stacks such as Iceberg, Parquet, Kafka, Pulsar, Spark or Flink.
• A track record of reducing friction in self-serve product adoption and of turning usage signals into commercial opportunities.
• Experience running a design-partner or co-innovation program that measurably changed a roadmap.

What We Offer

• Opportunities for professional growth in a fast-paced, high-impact global technology organization.
• Direct collaboration with experienced technology leaders, engineers, enterprise customers and strategic partners.
• High autonomy and ownership in defining product priorities, with accountability for measurable customer and business outcomes.
• The opportunity to shape the future of a leading Industrial IoT platform and contribute directly to its commercial success.
• Exposure to cutting-edge technologies across Industrial IoT, AI, cloud computing, edge computing and advanced analytics.
• An international team and flexible remote or hybrid working.

• Own the product strategy, roadmap and business case for Data Operations, aligned with company objectives, customer needs and market opportunities.
• Define and track the metrics that matter — adoption, usage, reliability, customer satisfaction and commercial performance — and use them to steer decisions.
• Evangelize practical agentic AI use cases in Industrial IoT, internally and with customers.
• Run continuous discovery: weekly customer conversations, workshops, prototype validation, competitive analysis and structured feedback from our preview customers.
• Translate customer and market problems into clear product concepts, requirements, user stories and a prioritized backlog.
• Work daily with the Engineering leads and our Design team to shape solutions that balance customer value, technical feasibility, effort and time to market.
• Make transparent trade-offs between strategic initiatives, customer commitments, technical debt and platform work, and explain the reasoning behind them.
• Partner with Product Marketing on positioning, value propositions, launch plans and enablement material, and act as a credible technical voice in customer and partner conversations.
• Support Sales, Pre-Sales, Customer Success and Professional Services in enterprise opportunities, and bring what you learn there back into the roadmap.
• Coordinate releases across Engineering, Operations, Marketing, Sales, Customer Success, Professional Services, and Support, and follow adoption after launch to verify the outcome.
• Work as part of the Product Management team: share context and learnings, review and challenge your peers' decisions, and step in where another Product Manager needs support, so that the platform evolves as one coherent product.

• B2B software product ownership
• Platform, infrastructure, or developer-facing product experience
• Agentic AI fluency
• Experience building with LLMs and agents
• Understanding of tool calling, context, and memory
• Ability to evaluate agent reliability
• High agency and bias towards action
• Experimental approach to problem-solving
• Team player with ability to challenge and accept challenges
• Ability to explain complex topics simply
• Fluent in English
• Industrial or OT background (IoT protocols, edge computing, SCADA, complex event processing)
• Experience with lakehouse or streaming data stacks (Iceberg, Parquet, Kafka, Pulsar, Spark, Flink)
• Track record of reducing friction in self-serve product adoption
• Ability to turn usage signals into commercial opportunities
• Experience running design-partner or co-innovation programs

• Owned B2B software products, particularly in platform, infrastructure, or developer-facing areas.
• Experience across the full product lifecycle.
• Fluent in agentic AI in practice, including building with LLMs and agents.
• Understanding of tool calling, context and memory, why agents fail in long multi-step tasks, and how to evaluate reliability.
• Daily use of AI tools in personal work.
• High agency and bias towards action.
• Experimental mindset, designing small, fast experiments when answers are unclear.
• Team player, able to challenge peers openly and accept challenges.
• Ability to explain complex concepts simply to diverse audiences.
• Fluent in English.
• German language is useful but not required.
• Industrial or OT background (IoT protocols, edge computing, SCADA or complex event processing) is a plus.
• Experience with lakehouse or streaming data stacks (Iceberg, Parquet, Kafka, Pulsar, Spark or Flink) is a plus.
• Track record of reducing friction in self-serve product adoption and turning usage signals into commercial opportunities is a plus.
• Experience running a design-partner or co-innovation program that measurably changed a roadmap is a plus.