Data Analyst

Interact provides enterprise-grade intranet software that connects over three million employees to leading global names like Levi's, Domino’s, Teva Pharmaceuticals, and Technicolor. Our team of customer‑focused problem solvers are passionate about helping organizations to communicate better. We do this together by constantly working to improve every service and product we offer.

With offices in Manchester, New York, Dubai, Tulsa, and Warsaw, we operate across North America, EMEA, and Australia. Click on any of our vacancies and you’ll see one thing in common – they all begin with this message. Why?

Because at Interact we treat everyone with the same respect and honesty. Whether you’re a developer fresh out of college or a seasoned salesperson, we live the motto that we uphold for our customers: our people are our most valuable assets. A Little About You...

Experience working with SaaS commercial data: ARR, churn, MRR, pipeline, funnel metrics, and unit economics. Demonstrable experience reconciling data across disparate systems (CRM, finance/ERP, invoicing, marketing automation). Ability to work autonomously and deliver work that senior stakeholders trust without needing to verify—accuracy is non‑negotiable.

A collaborative mindset with the confidence to work alongside experienced executives, taking steer on domain‑specific questions while owning the analytical process end to end. Excellent communication skills and executive presence; comfortable presenting to C‑suite and private equity audiences. Strong academic background (quantitative degree preferred: Mathematics, Statistics, Economics, Computer Science, Engineering, or similar).

Desirable Experience in a PE‑backed SaaS environment or with investor reporting. Proficiency in data visualisation tools (Power BI, Tableau, Looker) for dashboards and KPI reporting. Knowledge of ETL workflows and data modelling concepts.

Experience with Salesforce, HubSpot, and Sage or similar finance systems. Familiarity with forecasting, capacity planning, or operational KPIs (utilisation, SLA performance, customer health). About The Role Marketing & Sales Funnel Analysis Dive deep into multi‑system funnel data (HubSpot, Salesforce, web analytics) across millions of rows, examining individual records to identify inconsistencies, duplications, and data‑quality gaps.

Build scripted, automated analysis pipelines (Python, SQL) to deduplicate, aggregate, and present funnel data in new ways that reveal patterns, trends, and blind spots. Identify what is not being captured or pulled through across systems—surfacing the gaps as rigorously as the patterns. Undertake exploratory analysis across marketing and commercial data to test new hypotheses, evaluate emerging channels or approaches, and surface insights beyond established reporting.

Develop and maintain clear, accurate views of the end‑to‑end marketing and sales funnel that leadership can trust implicitly. Financial & Revenue Analysis Analyse ARR movements, customer churn, the ARR snowball, and P&L data to provide a clear, auditable picture of financial health. Connect and reconcile data across Sage (finance system), the homegrown invoicing system (line‑item detail), and Salesforce (opportunities, deals won/lost).

Support private equity reporting requirements with precise, investor‑grade analysis and data pack. Link product usage data to commercial outcomes (expansion, contraction, churn) to inform strategic decisions. Data Engineering & Automation Build and maintain automated reporting and data‑processing pipelines using Python, SQL, and appropriate tooling.

Develop automated data‑quality checks and reconciliation processes across systems. Create repeatable, well‑documented scripts and workflows that reduce manual effort and eliminate human error. Stakeholder Collaboration Work directly with the CEO, CFO, and the wider executive board, as well as private equity representatives, taking direction on priorities and translating business questions into analytical frameworks.

Leverage the deep functional expertise of senior stakeholders—across finance, operations, sales, and product—to ensure analysis is grounded in the right business context. Present findings and recommendations with the rigour and precision expected by senior financial and investment stakeholders. Promote data literacy across the organisation by guiding stakeholders on interpreting insights and using dashboards.

Operational & Cross‑Functional Support Partner with Operations and Customer Success teams to improve forecasting, capacity planning, and process effectiveness. Conduct root‑cause analysis on operational issues and present findings to leadership. Collaborate with Product and Engineering to ensure data flows and models support business needs. #J-18808-Ljbffr