[Hiring] Senior Marketing Data Analyst @AF

Role Description

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.
• 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.
• 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.
• 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.

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.

Qualifications

• 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.
• 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.