The Opportunity 

Cognism has built one of the most trusted B2B datasets in Europe. As we turn that data into market-leading products, we need a product manager who can own data products end to end - from understanding what customers are trying to achieve, to shaping what we build, to making sure it lands and creates value. 

This is a strategic role, where your leverage comes from the quality of your product decisions and your ability to align others around them. You will own a set of data products, be accountable for their quality, and be the person who turns a customer problem into a clear, buildable product that people genuinely want to use. The starting point is always the customer: what problem they have, what they have tried, and what would change their workflow. 

What This Role Owns 

• The strategy for your product area - where it's headed, what to bet on, and how it ladders up to the wider product vision. 

• The data products in your area - what they are, who they serve, and how they earn their place in a customer's workflow. 

• The customer use cases behind those products - which segments benefit, in which workflows, and to what outcome. 

• The quality of the data assets you own - accuracy, coverage, freshness, and delivery reliability - and the judgement of what is good enough to put in front of customers. 

• The delivery roadmap for your products - what you build, in what order, and what you deliberately leave out. 

• Adoption and value - proving that what you ship solves the problem it was meant to, not just that it shipped. 

What This Role Will Do 

1. Data product ownership and delivery

• Own the product development cycle for your data products, from discovery and validation through to launch, adoption, and iteration. 

• Define requirements, set priorities, and make clear scope calls - what a product should and should not do, and why. 

• Work closely with data engineering and data science to shape data assets that can be delivered at the highest quality - so customers can build on data they trust - across CRMs, APIs, and agentic systems. 

• Ground every product decision in a customer use case and a commercial outcome, not just technical capability. 

• Define the success metrics and KPIs for your products up front, then measure outcomes continuously - using the data to drive decisions and iterate. 

• Shape how your products are packaged and priced - working with commercial teams so the way data is offered matches how customers buy and where they see value. 

1. Customer understanding

• Turn customer needs into clear data requirements that engineering and data teams can build against. 

• Validate that what you build creates value - decide up front how you will know a product is working, and test it with real customers. 

• Partner with Sales and Customer Success so customers realise the value of the data, and feed what you learn back into the product. 

1. Data quality and governance

• Own the quality of the data assets in your area and set the standard customers can rely on. 

• Decide how to surface confidence, freshness, and match quality to customers where it affects trust and adoption. 

• Build GDPR compliance and data governance into the product from the start. 

• Put evaluation frameworks in place - specific to data products - to measure data quality objectively and improve it continuously, so you can prove the data meets the standard customers rely on. 

• Work with data sourcing teams to expand and strengthen the datasets behind your products - deciding what to source, build, or partner for. 

1. Stakeholder Management

• Act as the primary point of contact for your product area across Engineering, Data Science, Sales, Customer Success, Commercial, and Compliance — translating between technical and commercial language as needed. 

• Manage dependencies with product peers who own adjacent surfaces; where priorities conflict, resolve disagreements directly and drive to a decision without escalation unless necessary. 

• Keep senior stakeholders (e.g. leadership, commercial leads) informed of roadmap trade-offs and progress, proactively surfacing risks before they become blockers. 

• Build trust with customer-facing teams (Sales, CS) so they can represent your product accurately and feed real customer signal back into your roadmap. 
• Where a decision affects multiple teams, take ownership of driving alignment — running the discussion, not just attending it. 

1. Market awareness

• Keep a current view of the B2B data market - how it is evolving, who the key players are, and what customers are choosing and why. 

• Use that view to sharpen your products and spot where Cognism's data can create differentiated value. 

What We Are Looking For 

A specialist data product person - not a generalist - who has owned a data product end to end: defined what it needed to be, built it, evaluated it, brought it to market, and can speak to the customer impact it created. You understand how data and models work, lead with curiosity about the customer, and land with real outcomes. 

Must-Haves 

• 5+ years in product roles, with direct experience owning data products or data-driven features in a B2B SaaS or technology business. 

• Proven ability to define and own data use cases - mapping customer segments, workflows, and outcomes into a clear product direction. 

• Experience setting product strategy for an area you own - deciding where to focus, what to bet on, and what to say no to. 

• Direct experience treating data as a product: you have defined a data offering, built it, brought it to market, and can speak to the impact. 

• Commercially minded - you understand how customers buy and what drives value for them, and your instinct is to build the right product. 

• A track record of customer discovery - interviews, research, or feedback loops that directly shaped a product direction. 

• Technically fluent enough to work credibly alongside engineering and data teams.  

• Hands-on experience building ML-driven data features - you don't need to be a data scientist, but you understand how models, enrichment, and signals work, can evaluate their quality, and can shape products around them. 

• A strong cross-functional collaborator who can bring product, engineering, and commercial teams into alignment. 

• A clear communicator who can make a complex data proposition simple for a customer.  

• Able to work with compliance and security experts to build compliance and security in by design.  

Nice-to-Haves 

• Experience in B2B sales intelligence, intent data, contact data, or a related data category. 

• Experience in a scale-up or high-growth environment where you have built strategy and execution at the same time. 

• Experience delivering data through APIs, MCPs, or integrations - building for programmatic and agentic consumption, not just a UI. 

• Experience monetising or packaging data products - shaping pricing and usage models for how data is bought. 

• Familiarity with the go-to-market tech ecosystem - the CRM and sales and marketing tools our data plugs into.