eneba - Data Analyst - Customer Support
Requirements
• 3+ years of experience as a Data Analyst in Customer Support, contact-centre or CX environments. • Strong SQL and hands-on experience with BI and reporting tools (we use QuickSight; Power BI, Looker or similar is fine). • Solid knowledge of customer support metrics and contact-driver analysis: CSAT, FCR, handling time, repeat contact, deflection. • Ability to turn insights into operational changes with non-technical stakeholders. • Curiosity about why customers get in touch and what would stop the issue from happening again. You care more about changing outcomes than producing dashboards. • Good written and spoken English (proficiency preferred). • Python or text analytics on ticket data. • Experience evaluating chatbots or AI agents. • Background in digital goods or gaming. • What it’s like to work at Eneba • Opportunity to join our Employee Stock Options program. • Opportunity to help scale a unique product. • Various bonus systems: performance-based, referral, additional paid leave, personal learning budget. • Paid volunteering opportunities. • Work location of your choice: office, remote, opportunity to work and travel. • Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes. • Please attach CV's in English. • To find out about how we handle your personal data, make sure to check out our Candidate Privacy Notice https://www.eneba.com/candidate-privacy-notice
Responsibilities
• Daily operations pulse: Monitor volumes, backlog, SLAs, and CSAT to flag spikes early. • Contact-driver analysis: Identify driver reasons by product and spot growing issues. • Weekly reporting: Deliver support performance packs with actionable insights for ops managers. • Automation & self-service: Measure AI, bot, and help content resolution, repeat contacts, and CSAT. • Quality & feedback: Analyze QA and feedback to surface coaching themes and process gaps. • Root-cause partnership: Partner with Product, Payments, and Risk to quantify issue impact and track fixes. • Planning input: Help forecast contact volume and capacity needs. • Data quality: Audit contact tagging to ensure reliable reporting. • Metric design & implementation: Establish core support performance metrics—such as repeat contact, automated deflection rates, and cost per contact—and publish reports featuring actionable thresholds for leadership.
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