Marketing Data Analyst - Mid Level

Owns a defined analytics domain (e.g., campaign performance, lifecycle/email analytics, or paid media) end to end — from data pull through insight to a stakeholder-ready recommendation. This role is expected to interpret data, not just report it, and to communicate directly with US-based marketing leads with minimal hand-holding.

Key Responsibilities

• Own reporting and analysis for an assigned channel or program area (e.g., campaign performance, deposit/product marketing, lifecycle marketing)
• Build and maintain dashboards and automated reporting in GA4 and Salesforce Marketing Cloud, reducing manual reporting effort over time
• Translate raw performance data into a clear "what it means" narrative for marketing stakeholders — not just charts
• Support multi-touch attribution analysis and campaign ROI tracking
• Partner directly with US-based marketing managers to define reporting requirements and answer performance questions
• Identify data quality or process gaps and propose fixes rather than working around them
• Mentor/QA the work of Jr. Analysts on the team

Required Qualifications

• 2–4 years in marketing analytics, digital analytics, or a similar data-facing marketing role
• Proficient in GA4 and Salesforce (CRM and/or Marketing Cloud)
• Working SQL proficiency (can independently query, join, and validate data)
• Experience building dashboards in a BI tool (Tableau, Looker, Power BI, or GA4/Salesforce native reporting)
• Demonstrated ability to work independently against ambiguous requests and clarify scope proactively
• Excellent written English; comfortable presenting findings in writing or over video to US stakeholders

Preferred Qualifications

• Experience with marketing attribution models, particularly in B2B or long sales-cycle environments
• Financial services, banking, or fintech experience
• Experience with Adobe Workfront or campaign/project management tooling
• Exposure to multi-entity or multi-brand marketing environments

Success Metrics

• Reduction in manual reporting effort (automation rate)
• Quality and adoption of insights by stakeholders (are recommendations acted on?)
• Turnaround time on stakeholder requests
• Accuracy and defensibility of attribution/ROI reporting