Operations Data Analyst

Multiverse
LondonPosted 26 March 2026

Tech Stack

Job Description

Operations Data Analyst Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance. In June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post-money valuation of $1.7bn, the round makes us the UK’s first EdTech unicorn. But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output. Join Multiverse and power our mission to equip the workforce to win in the AI era. Reporting to the Senior Manager, Analytics & Insights, this role sits in the wider operations organisation. This is a pivotal role in optimising the operations and efficiency of our Operations organisation. The individual will use data analytics, organisation and process management skills to improve Multiverse’s operations. This team will work closely with stakeholders within the Operations organisation and the Data & Insights organisation to deliver metrics, dashboards and insights, and improve the accuracy of our data. KEY RESPONSIBILITIES - Coordinate large team communication & reporting forums across Operations to increase change adoption, transparency and collaboration. - Develop and maintain dashboards, deliver critical insights for the Operations team in partnership with the Data & Insights team to track key performance indicators (KPIs) and support the execution of daily operations. - Assess and analyse data to develop and determine strategies for accelerating progress, proactively identify areas for improvement, and deliver on roadmap priorities and personal OKRs - Support with ad-hoc strategic project management within Delivery including research and coordination. - Complete ad hoc analysis in support of continuous improvement efforts (as opposed to D&I functional and upward reporting) - Codify standards for projects (templates, processes, etc), pilots and experiments - Work with leaders to assess project plans and identify testing opportunities based on research and analysis - Establish and maintain a centralised roadmap for pilots, experiments and projects within Delivery - Manage the combined delivery & ops action plans / OKRs as a comprehensive execution tracker and unified view of all initiatives with Delivery. - Develop and drive initiatives to improve the quality and health of data, so stakeholders can trust it SUCCESS METRICS - Metric lift: Sustained improvement on your KPI (with baselines, deltas, and clear attribution). - Dashboard creation: Delivering accurate dashboards that aid business outcomes and driving adoption of the same. - Automation impact: Fewer manual touches and faster cycle time in your workflows, with auditable controls. - Clean, reliable data: Clear lineage, minimal rework, and stakeholder trust trending up. - Cross-functional Collaboration: Positive peer feedback and 360-degree reviews from primary, pre-identified cross-functional stakeholders. - OKR Achievement: Demonstrated accomplishment of individual and team Objectives and Key Results (OKRs), assessed both objectively and through self-evaluation. SKILLS & QUALIFICATIONS - Extreme bias to action: You ship fast, learn in public, and iterate weekly; you balance speed with quality and compliance. - Experience: ~3-6 years of professional work experience; ideally 1+ year of experience within a data analyst role in a data centric organisation. You have competently used the following tools for analysis/visualisation: - Essential: Excel/Go ... (truncated, view full listing at source)
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