Responsibilities

• Analyse the utilisation of audiences (including Lookalike audiences), and platform performance data to uncover optimisation opportunities across bidding, budgets, and testing.
• Design and validate measurement signals through clean A/B testing (bonus: reconciling platform data against source-of-truth).
• Own the paid social data infrastructure - building dbt pipelines and monitoring pipeline health to ensure data reliability.
• Build, maintain, and audit BI dashboards (e.g. Looker, Lightdash) so stakeholders can self-serve reliable performance data.
• Develop reusable analytical frameworks to quantify budget optimisation potential and independently trace anomalies to root cause.
• Partner closely with Paid Social Managers, translating insights into strategy and proactively flagging risks and trade-offs.
• Collaborate cross-functionally with Marketing Platform, Data Science, Marketing Science, and Analytics Engineering, staying agile to shifting dependencies.
• Stay current with paid social trends in optimisation, tracking, and measurement.

Requirements

• 2+ years in a data/analytics role within the marketing domain, with hands-on experience designing and validating experiments (e.g. test-vs-control setups).
• Advanced SQL - comfortable writing multi-CTE queries against a cloud warehouse (Snowflake/BigQuery) unaided.
• Proficiency in Python or R.
• Experience building and maintaining dashboards/reports in a BI tool (e.g. Lightdash, Tableau).
• Data quality mindset - auditing and fixing data discrepancies.
• Familiarity with different attribution models and a general understanding of the limitations and trade-offs involved in measuring marketing performance.
• Core unit economics fluency - CAC, LTV and contribution margin.
• Strong stakeholder communication - comfortable explaining complex analysis to non-technical audiences and raising concerns or trade-offs proactively.

Preferred

• Proven dbt pipeline experience.
• Familiarity with major paid social platforms (Meta, TikTok, Reddit, Snapchat).
• Saturation/response curve modelling and budget optimisation experience.
• Understanding of incrementality testing and Marketing Mix Modelling.
• Experience with event-delivery and server-side tracking tools (e.g. Hightouch, CAPI) and click-ID based measurement (fbclid, ttclid, rdt_cid, sccid).
• Experience with MMPs (Singular, AppsFlyer, Adjust).

Benefits

• Base salary of £60,000 - £85,000
• RSU's in a growing and public company
• Work from (almost) anywhere in the world for up to 90 days a year
• Flexible working - you’re trusted to do the right thing and be responsible
• Private Medical Insurance + Life Insurance
• Discounted gym memberships and cycle to work scheme
• A paid 6-week sabbatical leave after four years
• 26 weeks maternity leave at full pay
• An annual self-development budget
• Annual Mission Days festival