Senior Marketing Data Analyst

ApprovalMax

22h•Remote

About The Position

ApprovalMax provides end-to-end accounts payable automation for businesses using cloud accounting systems such as Xero and QuickBooks Online. We have 20,000+ businesses worldwide across 70+ countries, and strong product-market fit built over 8+ years. Our customers, accountants, bookkeepers, and finance teams are risk-averse, non-technical users who rely on us to control how money leaves their business. This is a dual role, you will do serious analytical work, building the models, dashboards, and insights Marketing relies on, and you will own how Marketing uses data, setting the rhythms, SLAs, and standards that make analytics a genuine partner to the business rather than a service desk. The measurement layer underneath Marketing (attribution, tracking taxonomy, UTM standards, the HubSpot property schema, and the definitions behind the funnel numbers) has grown organically and needs to be rebuilt deliberately. You will be the person who does that, and then keeps it honest. This isn’t a role where you’ll be handed a brief and asked to execute. You’ll help shape how embedded analytics works at ApprovalMax, with real influence over the model from day one. 80% marketing analytics, 20% wider data contribution, including mentoring, standards and best practices across the business.

Requirements

4+ years’ experience in data analytics, ideally across marketing, growth or GTM
Experience owning marketing measurement, attribution and tracking
Strong diagnostic skills across tracking, attribution and data quality
Hands-on use of AI/LLM tools in your analytical workflow
Strong SQL and Python skills, including production-quality code
Experience with GA4, GTM, CRM platforms (HubSpot preferred) and paid media data
Experience with BI/product analytics tools such as Lightdash, Looker or Amplitude
Ability to combine data across CRM, product, finance and marketing
Strong communication skills with senior, non-technical stakeholders
Experience with metric governance and resolving conflicting data
Comfortable working with ambiguity and turning complex questions into actionable insights

Nice To Haves

SaaS/B2B experience and familiarity with subscription & PLG metrics.
Experience with product-led growth, activation and conversion analysis
Familiarity with dbt, semantic layers and data modelling
Experience with server-side tracking or dataLayer implementation
Exposure to experimentation, incrementality or media-mix modelling
Experience with embedded analytics models
Exposure to LLMs, text-to-SQL or AI-driven data exploration
Experience mentoring analysts or setting analytical standards

Responsibilities

Own the analytical relationship with the Marketing 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 Marketing relies on, and take responsibility for their accuracy, freshness, and usability.
Own the analysis behind the funnel that matters most to the business: visitor → free trial → paid conversion, trial nurture performance, lifecycle-stage progression, and the leading indicators that move them.
Quantify channel and campaign performance end to end (spend, CAC, payback, pipeline contribution and retained revenue) rather than stopping at platform-reported conversions.
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 Marketing analysis, combining product usage, subscription revenue, and CRM data to surface insights a channel-level view would miss.
Own the rebuild of marketing attribution: agree the model with Marketing and Finance, document its assumptions and known blind spots, and implement it in the warehouse so that one number is produced once and reused everywhere.
Reconcile platform-reported numbers (ad platforms, GA4, HubSpot) against warehouse truth, and be the person who can explain — credibly and repeatedly — why they differ.
Build the measurement approach for activity that attribution cannot see cleanly: brand, organic, partner and word-of-mouth.
Incrementality and holdout testing where the spend justifies it.
Design and analyse marketing experiments — landing pages, nurture sequences, pricing and packaging messages — including how success is defined before the test runs.
Diagnose the tracking we already have, not just the tracking we intend to build: read the existing client-side implementation, cookie and local-storage handling, and referrer behaviour to work out why a channel is misreporting.
Own the event taxonomy: what we track, what each event means, what properties it carries, and what it is allowed to be used for.
Own the Google Tag Manager container (tags, triggers, variables) and the QA process that proves tracking works before and after a release.
Own the UTM naming standard and the automated compliance checks that enforce it.
Specify the dataLayer contract that the website must expose, and the requirements for server-side tracking.
Own tracking QA as a standing process, not a one-off project: broken tracking should be detected by a check, not by someone noticing a dashboard looks wrong three weeks later.
Own the custom property schema in HubSpot from a data perspective: which properties exist, what they mean, who writes them, and how they map to the warehouse model.
Own the Marketing domain within the Central KPI & Metrics Glossary — MQL, SQL, opportunity, trial, activation, conversion, retention — and bring additions and conflicts to the governance process rather than letting shadow definitions accumulate.
Make sure that when Marketing, Sales, Finance and Product quote the same metric, they are quoting the same number, and that where they legitimately differ, the difference is documented rather than argued about.
Ensure new product features, pricing changes, or GTM motions are reflected in metric definitions and dashboards ahead of launch, not after.
Define and document the working model between the Data Team and Marketing: metric ownership, request intake, turnaround SLAs, escalation paths, and data freshness expectations.
Establish an explicit fast lane for time-critical marketing requests, so that urgent work can be pulled into the current week rather than waiting for the next planning cycle — and be honest, in writing, about what does not qualify.
Document domain knowledge, stakeholder preferences, and analytical edge cases so marketing analytics can be handed off or scaled without starting from scratch.
Build and maintain dashboards and automated reports (Lightdash, Amplitude, GA4) so Marketing can answer recurring questions without an analyst in the loop.
Write clear documentation others can verify, extend, and build on: metric definitions, methodology notes, and data caveats.
Actively shift recurring analytical work to self-service, freeing your capacity for higher-value analysis.
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.
Help embed agentic workflows within the Marketing team, showing stakeholders how to get answers from data directly.
Contribute as a practitioner and user of the natural-language and text-to-SQL tooling being built by the Data Platform Lead.
Flag data quality issues upstream to the Data Platform Lead; understand how your analyses depend on specific pipelines and contracts, and raise risks proactively.
Treat marketing data quality as your own problem where it originates in taxonomy, tagging, or CRM property design — that is your surface, not the platform team's.
Participate in the intake and weekly triage process; help classify and scope incoming requests from Marketing.
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.