We are seeking an experienced Data Analytics Lead to join a large and complex organisation on an initial 6-month fixed-term contract, covering a period of maternity leave, with potential for extension.

This is a senior individual contributor role within a small and collaborative Data & Insights team. You’ll work across a range of firm-wide initiatives, combining hands-on data analysis with senior stakeholder engagement.

We’re looking for someone who can hit the ground running, take ownership and proactively identify where data can create value – rather than simply waiting for work to come to them.

What you’ll be doing

• Deliver commercial analytics, insights and recommendations across a range of business initiatives
• Investigate datasets to identify trends, anomalies and previously unseen insights
• Translate complex analysis into clear data stories that influence business decisions
• Work closely with senior stakeholders, adapting your communication and approach to different audiences
• Take ownership of analytical projects from initial discovery through to delivery and recommendation
• Proactively identify business problems and opportunities where data can make a difference
• Support a variety of initiatives across Amazon Connect, HubSpot CRM, Workday, SharePoint, Finance and Innovation
• Troubleshoot reporting issues, data relationships, KPIs and underlying data problems
• Partner with Data Engineers and leverage your understanding of data architecture and curated data layers
• Contribute to data governance, quality and analytics best practice
• Provide guidance and mentorship to junior analysts and share knowledge across the team

About you

You’ll be an experienced Data Analyst, Commercial Analyst, Business Analyst or Analytics professional who enjoys getting beneath the surface of the data.

You will ideally have:

• 5+ years’ experience in hands-on analytics, commercial analytics or a similar role
• Strong SQL skills and experience working with relational data
• Experience with Power BI and/or Qlik
• Strong analytical and investigative problem-solving skills
• Proven experience uncovering insights from complex or imperfect datasets
• Strong commercial acumen and the ability to connect analysis to business outcomes
• Excellent stakeholder management and communication skills, including the ability to influence senior stakeholders
• The ability to translate complex “data speak” into clear and meaningful business language
• End-to-end experience taking analytical problems from discovery through to actionable recommendations
• An understanding of data architecture and how data moves through an organisation
• A proactive, curious mindset and the ability to work autonomously
• A collaborative approach and the ability to work effectively within a small team

SUMMARY OF POSITION 

The Business Intelligence & Analytics 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. 
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