Data Analyst

About the company

Interact builds enterprise intranet software used by more than three million employees at well-known global businesses including Levi's, Domino’s, Teva Pharmaceuticals, and Technicolor. The company focuses on helping organisations communicate more effectively and operates with teams in Manchester, New York, Dubai, Tulsa, and Warsaw across North America, EMEA, and Australia.

The culture emphasises respect, honesty, and the belief that people are the company’s most important asset, regardless of experience level or role.

Role overview

Interact is hiring a Data Analyst who will work in a highly hands-on, technically involved way across commercial and finance data. This position goes far beyond standard dashboard reporting: the main focus is row-level investigation across millions of records, including sampling, validation, scripting, and deep analysis to produce a reliable picture of marketing and sales performance, revenue trends, and financial results.

The business already has core metrics and funnel definitions in place. Your work will involve strengthening and refining those existing structures, while also exploring new patterns, testing fresh hypotheses, and uncovering insights that are not yet part of standard reporting. The role will move between structured reporting and open-ended investigation depending on business needs.

You will work closely with the CEO, CFO, executive leadership, and private equity stakeholders. The expectation is that you translate their questions into rigorous analysis, deliver automated outputs they can trust, and maintain a high standard of accuracy throughout.

Key responsibilities

• Analyse SaaS commercial metrics such as ARR, churn, MRR, pipeline activity, funnel conversion, and unit economics.
• Reconcile information across separate systems, including CRM, finance/ERP, invoicing, and marketing automation platforms.
• Investigate large multi-system funnel datasets using tools such as HubSpot, Salesforce, and web analytics, identifying inconsistencies, duplicates, and missing data.
• Build automated analysis workflows in Python and SQL to deduplicate, aggregate, and present funnel data in useful formats.
• Spot data that is not being captured correctly or is dropping out between systems, and highlight those gaps as carefully as the trends.
• Carry out exploratory analysis to test new ideas, review emerging channels or approaches, and surface insights beyond established reports.
• Maintain reliable end-to-end views of the marketing and sales funnel that senior leadership can depend on.
• Analyse ARR movement, customer churn, the ARR snowball, and P&L data to create an auditable view of financial performance.
• Connect and reconcile data from Sage, the company’s own invoicing system, and Salesforce opportunity/deal data.
• Support private equity reporting with accurate, investor-ready analysis and data packs.
• Link product usage patterns to outcomes such as expansion, contraction, and churn.
• Develop and maintain automated reporting, processing pipelines, and data-quality checks.
• Create reusable, documented scripts and workflows that reduce manual work and lower the chance of human error.
• Work with the CEO, CFO, executive board, and private equity representatives to turn business questions into analytical frameworks.
• Use the experience of senior stakeholders across finance, operations, sales, and product to ensure analysis is grounded in business reality.
• Present insights and recommendations with the level of precision expected by senior financial and investment audiences.
• Help improve data literacy across the organisation by supporting stakeholders in interpreting dashboards and findings.
• Partner with Operations and Customer Success teams to improve forecasting, capacity planning, and overall process effectiveness.
• Investigate operational problems at the root cause level and share findings with leadership.
• Collaborate with Product and Engineering to make sure data flows and models support business requirements.

Candidate profile

The ideal applicant will have experience handling SaaS commercial data and working with row-level datasets at scale. Accuracy is critical, and the successful person should be able to work independently in a way that gives senior stakeholders confidence in the results.

Strong communication skills and executive presence are important, along with the ability to collaborate with experienced leaders while owning the full analytical process end to end. A solid academic background is preferred, especially in a quantitative discipline such as Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field.

Requirements

• Hands-on experience with SaaS metrics such as ARR, churn, MRR, pipeline data, funnel reporting, and unit economics.
• Proven ability to reconcile and validate data across different systems.
• Confidence working independently and producing analysis that senior leaders can trust without rechecking.
• Ability to collaborate with executives while still taking ownership of the analytical process from start to finish.
• Strong communication skills and the ability to present effectively to C-suite and private equity audiences.
• A strong academic background, ideally in Mathematics, Statistics, Economics, Computer Science, Engineering, or a similar quantitative subject.
• Comfort working with large datasets and row-level investigation.

Desirable experience

• Previous work in a PE-backed SaaS company or on investor reporting.
• Experience using data visualisation tools such as Power BI, Tableau, or Looker.
• Understanding of ETL processes and data modelling concepts.
• Familiarity with Salesforce, HubSpot, Sage, or comparable finance systems.
• Exposure to forecasting, capacity planning, or operational KPIs such as utilisation, SLA performance, and customer health.

Tools and technical focus

This role relies heavily on Python and SQL for automation, analysis, and data processing. Experience with dashboards and reporting platforms is useful, especially where they support KPI reporting and executive decision-making.

Additional information

This is a full-time, onsite role based in Manchester, England, United Kingdom. No salary, stipend, number of openings, start date, or application deadline was specified in the source information.

No internship duration is applicable for this position.

There were no separate perks, eligibility details, or vacancy count provided beyond the responsibilities and candidate profile above.