Senior Marketing Data Analyst

Tracking Architecture & Data Collection Standards

• 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. Our largest single attribution problem today is an oversized Direct segment, and unpicking it is an early priority for this role.
• 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. Per-campaign tagging is done by whoever launches the campaign; you own the standard, the validation, and the report that shows who is out of compliance.
• Specify the dataLayer contract that the website must expose, and the requirements for server-side tracking. You write the spec and verify the result; the implementation in site code and the infrastructure underneath it are built by web engineering.
• 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.

Marketing Data Model & Metric Definitions

• 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. Specify the fields; workflow build sits with Revenue Operations.
• 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.

Ways of Working with Marketing

• 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.

Self-Service, Reporting & AI-Assisted Workflows

• 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. You should be faster and more thorough because of these tools, not merely aware of them.
• 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.

Data Quality, Governance & Mentorship

• 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.

What We're Looking For

Must Have's:

• 4+ years of hands-on experience in a data analyst role, including significant time analysing marketing, growth, or GTM funnel performance for senior stakeholders.
• Owned a marketing measurement stack end to end: attribution modelling, event taxonomy, UTM governance, and the reconciliation between platform-reported and warehouse numbers.
• Diagnostic depth in existing tracking implementations. You can read a site's tracking code, cookie and local-storage logic, and referrer handling well enough to explain why a channel is misreporting, and then specify the fix. Concretely: given an unexplained Direct segment absorbing a large share of conversions, you know how to establish the cause rather than assume one.
• 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.
• Hands-on with the marketing data stack: GA4, Google Tag Manager, a CRM (HubSpot strongly preferred), and the major paid channels' reporting.
• Experience with modern BI and product analytics tools — Lightdash, Looker, Amplitude, 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