Senior Marketing Data Analyst

Job description

About the Role We are looking for a Senior Marketing Data Analyst to own our marketing data pipeline end-to-end, from extraction, through transformation in BigQuery, to data analysis and client-ready dashboards in Tableau. This is a high-trust, high-autonomy role. You'll be the last line of defense before data reaches clients and internal stakeholders, so precision, proactive communication, and independent problem-solving matter just as much as technical skill. What You'll Own Data Pipeline & Extraction Manage data extraction from ad networks, MMPs (Adjust, AppsFlyer), and analytics tools (GA4) via Improvado into Google BigQuery. Monitor pipeline health proactively, catch and flag broken feeds, delayed loads, or schema changes before they affect downstream reporting, not after a client notices. Data Transformation & Modeling Build and maintain SQL transformations and data models in BigQuery, establishing clean, correct relationships across multiple data sources. Write documented, reusable queries and scripts (SQL, Python and/or R) rather than one-off fixes. Data Analysis & Insights Calculate and model LTV by cohort, channel, and campaign; own churn calculation and reporting. Build predictive models for churn and LTV to support proactive retention and budget decisions. Run cohort and retention curve analysis to track user quality over time. Analyze CAC and monitor CAC:LTV ratios to guide acquisition spend; measure ROAS/ROI by channel and campaign. Conduct funnel and conversion drop-off analysis to identify where users are lost. Support attribution modeling (multi-touch/incrementality) to clarify true channel contribution. Produce revenue, spend, and user-growth forecasts; contribute to media mix and budget allocation modeling. Dashboarding & Visualization Design and maintain interactive Tableau dashboards that blend multiple data sources with correct joins and relationships. QA every dashboard and report for accuracy before it reaches a client or stakeholder, numbers tie out, filters work, nothing is stale. Quality & Ownership Take full ownership of data accuracy from source to dashboard; you self-check rather than relying on someone else to catch mistakes. Proactively flag anomalies, discrepancies, or risks to your manager and stakeholders as soon as you spot them, no surprises, no last-minute fire drills. Collaboration & Communication Partner directly with UA/performance marketers, clients, and stakeholders to define KPIs and refine reporting frameworks. Communicate clearly and promptly: status updates, blockers, and caveats are shared before they become problems, not after. What success looks like in your first 90 days: Full fluency with our Improvado → BigQuery → Tableau pipeline and current dashboard suite. Zero client-facing data-quality escalations traceable to preventable errors. At least one process improvement, automation, or QA safeguard you identified and implemented on your own initiative. What We're Looking For 5–8 years of experience in data analytics, ideally within marketing, digital advertising, or performance-driven environments. Strong SQL (BigQuery) and working proficiency in Python or R. Hands-on experience with Improvado or a comparable UI-based data extraction tool. Advanced Tableau skills, you can build multi-source dashboards with correct data relationships, not just single-table charts. Experience with Adjust, AppsFlyer, GA4, and major ad networks. Solid grasp of core marketing analytics metrics, LTV, CAC, churn, ROAS, retention/cohort analysis, and how to turn them into recommendations. Comfortable building basic predictive/statistical models (e.g., churn or LTV prediction) using SQL, Python, or R. A demonstrated track record of catching your own mistakes before they ship (be ready to talk through a real example in the interview). Comfortable working with minimal supervision: you flag problems and propose solutions rather than waiting to be asked. Excellent written and verbal English; you can explain data issues clearly to non-technical stakeholders. Bonus Points Experience designing and analyzing A/B tests. Familiarity with GCP tooling beyond BigQuery (Cloud Functions, Composer/Airflow, dbt). Deeper machine learning experience for predictive analytics (beyond core churn/LTV models). Additional languages beyond English.