Behind the scenes

We're restructuring the way we work with data, and we're doing it properly.

Instead of one central team trying to serve everyone, we're creating four dedicated analytics hubs — Core, Marketing, Product, and Revenue — each deeply embedded in the business area it supports. Each hub will have its own focused team, its own identity, and a real seat at the table.

We're hiring 1 senior analyst per hub (except the core team), and we want people who are ready to grow into something bigger.

Role Overview

Reporting to the Head of Data, you will own the analytical relationship between the central data hub and the respective function (Marketing, Revenue or Product). This is a dual role: you will do serious analytical work, building the models, dashboards, and insights your function relies on, and you will own how that function uses data, setting the rhythms, SLAs, and standards that make analytics a genuine partner to the business rather than a service desk.

This is not a role for someone who wants to be handed a well-defined brief and execute against it. The spoke model at ApprovalMax is being built now, and the person in this role will have real influence over what it becomes, first for their function and as a template for how the broader data team operates over time. You should be as comfortable defining how analytics works as you are doing the analytical work itself.

Key responsibilities

1. Function Analytics

• Own the analytical relationship with the Marketing, Revenue or Product function: understand their priorities, anticipate their data needs, and deliver analysis that drives decisions rather than merely informing them.

• Build and maintain the core dashboards, reports, and models that respective function relies on, and take responsibility for their accuracy, freshness, and usability.

• Translate ambiguous business questions into clear analytical frameworks; make assumptions explicit and deliver useful outputs before perfect data is available.

• Bring cross-functional context to function analysis, combining product usage, revenue, and operational data to surface insights that a single-function view would miss.

2. Spoke Model Ownership

• Define and document the working model between the data hub and respective function: metric ownership, request intake, turnaround SLAs, escalation paths, and data freshness expectations.

• Own the function domain within the Central KPI & Metrics Glossary, and bring additions and conflicts to the governance process rather than letting shadow definitions accumulate.

• Document domain knowledge, stakeholder preferences, and analytical edge cases so the function spoke can be handed off or scaled without starting from scratch.

• Contribute to headcount planning by identifying where analytical capacity will be needed in function as the business grows.

3. Self-Service & Reporting

• Build and maintain dashboards and automated reports (Power BI, Amplitude) to reduce reliance on the hub for recurring questions.

• Write clear documentation that others can verify, extend, and build on: metric definitions, methodology notes, and data caveats.

• Actively seek opportunities to shift recurring analytical work to self-service, freeing hub capacity for higher-value analysis.

4. AI-Assisted Analysis & Agentic Workflows

• Use LLM-assisted tools and AI coding agents as a default part of your analytical workflow - for data exploration, query generation, insight synthesis, and documentation. You should be faster and more thorough because of these tools, not merely aware of them.

• Help embed agentic workflows within the function team, showing stakeholders how to get answers from data without always needing an analyst in the loop.

• Contribute as a practitioner and user of the natural-language and text-to-SQL tooling being built by the Data Platform Lead.

5. Data Quality & Governance

• Flag data quality issues upstream to the Data Platform Lead; understand how your analyses depend on specific pipelines and contracts, and raise risks proactively.

• Participate in the intake and weekly triage process; help classify and scope incoming requests from function.

• Ensure that new product features, pricing changes, or GTM motions are reflected in metric definitions and dashboards ahead of launch, not after.

6. Mentorship & Ways of Working

• Provide technical guidance, analytical framing, and code reviews for junior analysts.

• Contribute to agile workflows (Kanban/Scrum) and planning sessions; keep your work visible and priorities explicit.

• Help shape the documentation standards and analytical norms that will apply as the team scales.

What We're Looking For

Essential

• 4+ years of hands-on experience in a data analyst role, with a demonstrated ability to work across multiple data domains and deliver analysis to senior business stakeholders.

• Demonstrated use of AI coding agents and LLM-assisted tools as a core part of your analytical workflow, not occasionally but by default. You should be able to show how these tools have changed the speed and depth of your work. We are building an agentic-ready data function, and this role needs to model that approach.

• Strong SQL skills and proficiency in Python for data manipulation, automation, and analysis, with experience writing production-quality code, not just ad hoc scripts.

• Experience with modern BI and product analytics tools — Power BI, Amplitude, Looker, or equivalent — including building dashboards that non-technical stakeholders actually use.

• Track record of delivering analysis that combines data from multiple source systems (CRM, product, finance, marketing) to answer questions no single team could answer alone.

• Ability to communicate findings clearly and concisely to senior non-technical audiences, in writing, verbally, and through visualisations.

• Experience working within a metric governance process: definitions, ownership, and resolving conflicts between competing numbers.

• Comfortable operating with ambiguity, able to scope an unclear question, make reasonable assumptions explicit, and deliver something useful before the perfect data exists.

Desirable

• Experience in a SaaS or B2B product company, with exposure to subscription metrics (ARR, NRR, churn, expansion), product usage analytics, and GTM funnel analysis.

• Familiarity with dbt or similar transformation frameworks; understanding of how semantic layers and data models underpin self-service analytics.

• Experience working in or helping to establish an embedded analytics model (spoke, business partner, or equivalent).

• Exposure to LLM application patterns, prompt design, text-to-SQL, or the use of language models for data exploration and insight summarisation.

• Experience mentoring junior analysts or setting analytical standards within a team.

What We Offer

• Growing international business with 10,000+ subscribers

• Regular performance-based compensation reviews

• 26 days of paid time off

• 1 additional day off for your birthday

• Remote office assistance

• Service-years recognition financial reward

Org Chart/Reporting line

• Reports to:

  • Head of Business Analytics

• Manages:

  • No direct reports initially; expected to mentor Junior Data Analysts