Data Insights Product Manager

SUMMARY OF POSITION

The Data Insights Product Manager is a senior individual contributor role accountable for two outcomes: turning the data held across Impos, OnTap Data and Ezy Systems into insights our customers cannot easily get anywhere else, and evolving Impos Analytics, OTD reporting and Ezy reporting into best-in-breed products. The role sits at the intersection of analysis and product. It requires the technical depth to interrogate data directly, the commercial judgement to know which findings actually matter to a customer, and the product discipline to build the capability that delivers those findings repeatably and at scale. AI is core to how this happens: both as the tool that accelerates the analysis, and as a capability customers experience directly inside the product. Insight in this role is not a report; it is a recommendation a customer can act on, delivered in a way that creates genuine customer stickiness. The role operates across Impos, OnTap Data and Ezy Systems, partnering closely with Product, Engineering, Customer Experience and Sales to align priorities and drive adoption.

KEY RESPONSIBILITIES

Data Understanding and Analysis

• Develop deep, first-hand fluency in the data assets across the group: what is captured, how it is structured, where it is reliable, and where it is not.

• Interrogate data directly to answer commercial and customer questions, rather than waiting for a report to be built.

• Use AI tools to accelerate analysis, pattern detection and anomaly detection as a normal part of the workflow.

• Select and apply analytical methods appropriate to the question, including trend and cohort analysis, segmentation, benchmarking, anomaly detection and forecasting.

• Establish and document definitions for core metrics so that a number means the same thing everywhere it appears.

• Assess data quality, completeness and lineage; identify the gaps that limit insight and drive remediation with the relevant owners.

• Know the limits of the data and say so, including when the evidence does not support the conclusion being asked for.

Insight Generation and Customer Value

• Convert analysis into clear, prioritised insights that tell a customer something they did not already know and can act on.

• Identify the patterns that predict customer value, risk and churn, and translate them into proactive intervention rather than post-event explanation.

• Develop benchmarking and comparative views that a customer cannot replicate from their own data alone, creating a durable reason to stay.

• Own the narrative layer, ensuring every insight is delivered with context, a clear implication, and a recommended action, including where that narrative is generated or assisted by AI.

• Package recurring insight into repeatable insight products rather than one-off analysis.

• Partner with Customer Experience and Sales to embed insights into customer conversations, business reviews and renewals, and present directly to customers where the insight warrants it.

• Measure whether insights are used and whether they change customer behaviour; refine or retire what does not land.

Product Ownership: Impos Analytics, OTD Reporting and Ezy Reporting

• Own the vision, roadmap and prioritisation for the next generation of Impos Analytics and OTD reporting, and for Ezy Systems reporting, holding each to a best-in-breed standard.

• Lead the roadmap and customer experience for migrating customers from current reporting to the new versions of these platforms, partnering with Engineering on delivery.

• Embed AI as a native product capability, not a back-end convenience, so customers directly experience AI-generated insight and recommendations inside the product.

• Define the problems worth solving, write the requirements, and make the trade-off calls on scope, sequencing and effort.

• Work with Engineering and Data to specify, test and release change; accept or reject delivered work against agreed outcomes.

• Set and defend the platform's design principles: self-service by default, fast, trustworthy, and usable by people who are not analysts.

• Reduce manual reporting effort across the group by productising recurring requests into repeatable platform capability.

• Benchmark each platform against comparable products in the market and close the gaps that matter commercially.

• Define and track adoption, engagement and satisfaction measures for each platform, and use them to steer the roadmap.

• Maintain a visible, credible roadmap that stakeholders across the three business units understand and trust.

Hands-On Delivery

• Build and maintain dashboards, models and analytical outputs directly, to a standard that can be put in front of a customer or an executive.

• Use AI tools to prototype quickly and test whether an insight or a product concept has value before committing engineering effort.

• Automate recurring analysis and reporting so that effort scales with value rather than volume.

• Investigate and resolve discrepancies in data and reporting outputs, escalating systemic issues to the relevant owner.

• Remain close enough to the detail to defend any number the role publishes.

Collaboration and Stakeholder Management

• Act as the connective point between data, product and the commercial teams, building the credibility to align priorities across the group.

• Translate between technical and commercial audiences, adjusting depth to suit the audience without losing accuracy.

• Work across Impos, OnTap Data and Ezy Systems, respecting the differences between each business while finding the common insight layer.

• Brief and enable internal teams so that insights are used consistently, not only when this role is in the room.

• Uplift data capability across the group through documentation, coaching and worked examples, and mentor analysts and reporting users informally.

Data Governance, Security and Compliance

• Ensure customer and commercial data is handled in line with privacy obligations, contractual commitments and group policy.

• Apply appropriate controls over access, retention and sharing, particularly where insight draws on data from multiple customers, and where AI tools process customer data.

• Ensure any comparative or benchmarked output is aggregated and de-identified to a standard that protects individual customers.

• Maintain a defensible audit trail for how published numbers are derived, including where AI has contributed to an output.

SKILLS AND EXPERIENCE

• Bachelor's degree in a data, analytics, commerce, engineering or related discipline, or equivalent practical experience.

• 5+ years working with data in a commercial setting, including experience where the output was consumed by customers or executives rather than by other analysts.

• Demonstrated ability to query and model relational data at scale, and to build reporting outputs on top of it.

• Practical, hands-on experience using AI tools (e.g. LLM-based analysis, AI-assisted product features) in a commercial analytics or product context, not just conceptual familiarity.

• Experience owning a reporting or analytics product end to end, including requirements, prioritisation and release.

• Strong analytical reasoning: able to choose the right method, quantify uncertainty, and distinguish a real signal from noise.

• Commercial literacy across revenue, retention, churn and margin, with the ability to connect a data pattern to a business consequence.

• Excellent written and verbal communication, with a track record of making complex data understandable to a non-technical audience.

• Product discipline: comfortable defining scope, declining low-value work, and making prioritisation calls with incomplete information.

• Working knowledge of data governance, privacy and security obligations relevant to customer data.

• Self-directed, comfortable operating across multiple business units and competing priorities without a direct reporting line into each.

• Experience in hospitality, retail, liquor or B2B software is advantageous but not essential.