Staff Data Analyst, Acquisition Marketing

MongoDB
Dublin; IrelandPosted 27 February 2026

Job Description

<p>We are seeking a Staff Analyst for Marketing Growth Analytics focused on marketing acquisition analysis. In this role, you will serve as a measurement and strategy partner across Marketing Operations, Growth Marketing, and Product—connecting brand advertising, paid acquisition, lifecycle marketing, and in-product growth into a single, ROI-driven measurement framework.</p> <p>You will lead advanced analytics across Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), causal inference, and experiment-driven incrementality to evaluate paid media impact across both upper-funnel brand outcomes (awareness, mindshare, reach, frequency) and lower-funnel growth economics (LTV, CAC, payback, and long-term ROI). This role is critical in establishing end-to-end growth measurement, ensuring brand investment builds durable demand and mindshare while efficiently translating into high-quality customers, sustained engagement, and revenue over time.</p> <p>This role can be based out of our Dublin office or remotely Ireland.</p> <h3>Responsibilities</h3> <ul> <li>Own marketing acquisition program measurement and reporting for all paid media channels (paid search, paid social, and display) across the full funnel—driving actionable insights across brand awareness, mindshare, and acquisition to optimize media investment, ROI, and executive decision-making</li> <li>Serve as a strategic analytics partner to Marketing Operations, Growth Marketing, Product, and Regional Marketing, aligning measurement across paid acquisition, lifecycle campaigns, and in-product growth surfaces</li> <li>Leverage MMM and MTA outputs to inform channel mix, budget allocation, frequency, and messaging strategy, translating model results into clear, actionable recommendations for Marketing and Finance<br>Own growth economics measurement, including LTV, CAC, payback period, marginal ROI, and cohort quality by channel, audience, and geography</li> <li>Design and analyze incrementality experiments and geo-tests (e.g., holdouts, lift studies, bid/creative/audience tests) to validate model-based insights and quantify causal impact</li> <li>Apply causal inference techniques to observational paid media data when experimentation is not feasible, accounting for bias, confounding, and selection effects</li> <li>Build scalable analytical pipelines and reporting tools that reconcile platform data, MTA, MMM, and experiment results, ensuring reproducibility and consistency of insights</li> <li>Conduct deep-dive analyses using complex SQL and analytical tooling to understand how paid media influences acquisition, product activation, retention, and long-term value</li> <li>Partner with Data Architecture, Data Engineering, and Marketing Operations to define tracking standards, taxonomies, and data models that enable robust paid media and growth measurement</li> <li>Collaborate with stakeholders to define KPIs and measurement frameworks for new channels, campaigns, and product-embedded growth initiatives—aligning on goals, attribution approach, and success criteria pre-launch</li> <li>Act as a thought leader in paid media measurement, setting best practices for experimentation, attribution, and ROI analysis, and mentoring analysts and partners across teams</li> </ul> <h3>Skills Attributes</h3> <ul> <li>7+ years of experience in Analytics, Data Science, or Marketing Analytics, with deep partnership across paid acquisition, growth marketing, and lifecycle teams</li> <li>Advanced SQL expertise for large-scale analysis and data modeling; Python or R proficiency for statistical analysis, experimentation, or modeling</li> <li>Strong domain knowledge of paid media ecosystems (paid search, paid social, display/programmatic) and their role across the full growth funnel</li> <li>Proven experience with MMM and/or MTA, including interpretation, validation, and application to budget allocation, channel mix, and investment strategy</li> <li>Hands-on experience with incrementality measurement and ... (truncated, view full listing at source)