FxPro

BI Analyst

  • Vollzeit
  • Limassol, Zypern
  • vor 1 Tag
Bewerben

Job description

We are looking for a BI Analyst with strong Power BI skills and experience in product analytics and fraud detection. You will design and maintain dashboards, monitoring tools, and analytical solutions that help measure product performance, make data-driven decisions and help the team spot suspicious patterns in user activity.

Responsibilities:

  • Build and maintain Power BI dashboards, data models, and DAX measures
  • Track product KPIs: acquisition, activation, retention, engagement, conversion, churn, LTV, and revenue
  • Perform ad-hoc analysis to support product launches, A/B tests, feature adoption, and user funnel optimization.
  • Conduct cohort, funnel, retention, and segmentation analysis.
  •  Help investigate fraud and abuse cases through data analysis - spot suspicious patterns and anomalies in user behaviour
  • Talk to stakeholders (finance, operations, product) to understand what they need and turn that into analytics solutions
  • Prepare and deliver reports to stakeholders, clearly summarizing key findings and offering data-driven recommendations to enhance processes and overall business performance
  • Work with data engineers to make sure data from source systems is accurate and well-organized
  • Spot inefficiencies in processes and suggest improvements


Requirements

  • Bachelor’s or Master’s degree in Mathematics, Statistics, Economics, Computer Science, or a related field
  • Proven experience in data analysis and statistical modeling
  • Proficiency in SQL (at least at a basic level)
  • Hands-on experience with Power BI at a Data Analyst level, including ETL processes, DAX and M language, data modelling, creating effective data visualizations and dashboards
  • Comfortable with core product metrics: DAU/MAU/WAU, retention curves, conversion funnels, activation, churn, ARPU/ARPPU, LTV, CAC.
  • Familiarity with A/B testing - how to design tests and read results
  • Awareness of common fraud / abuse patterns: multi-accounting, bonus abuse, fake registrations, scripted/bot activity, payment-related anomalies.


Nice to Have:

  • Python or R for ad-hoc analysis, automation, or basic anomaly detection.
  • Exposure to behavioral analytics, anomaly detection, or basic ML concepts.


Soft Skills:

  • Can explain complex data in simple terms to non-technical audiences
  • Collaborative - comfortable working across different teams
  • Detail-oriented and takes data quality seriously
  • Proactive in finding problems and suggesting solutions