The Data Analyst in the Customer Department plays a critical role in transforming raw data into actionable insights that enhance customer experience, improve key customer metrics, and support strategic decision-making across the organization. This position requires strong analytical skills, high data sensitivity, technical capability, and cross-functional communication.
Responsibilities
Data Analysis & Reporting
- Collect, extract, and process data from multiple internal sources using tools such as SQL, Python, and other data pipelines.
- Build, automate, and maintain dashboards to monitor key Customer metrics (Retention, CSAT, FCR, SLA, Ticket Volume, Churn risk, etc.).
- Monitor daily/real-time data to detect anomalies and proactively raise alerts when issues arise.
- Conduct deep-dive analyses to identify root causes and provide data-driven recommendations for Operations, Product, Risk, and other teams.
Forecasting & Data Modeling
- Develop forecasting models for ticket volume, churn, and customer lifecycle trends.
- Formulate hypotheses based on observed data patterns and validate them using statistical methods.
- Support business initiatives with insights, data simulations, and scenario modeling.
Cross-functional Collaboration
- Work closely with Product, Marketing and other teams to help them understand data and its implications for decision-making.
- Translate complex datasets into clear, simple, and visually compelling insights that stakeholders can easily understand.
- Participate in customer experience improvement initiatives and process optimization projects driven by data.
Qualifications
- 13 years of experience as a Data Analyst, BI Analyst, Customer Analyst, or related role.
- Experience in fintech, banking, or digital product environments is a strong advantage.
- Proficient in SQL and Python with experience writing and optimizing complex code.
- Hands-on experience with data visualization tools such as Power BI, Looker Studio, Tableau, Mode or Metabase.
- Good understanding of statistics, forecasting methods, cohort analysis, and customer analytics.
- Strong attention to detail with high sensitivity to numbers and data patterns.
- Excellent analytical thinking and problem-solving skills; able to propose solutions grounded in data.
- Effective cross-functional communication; able to explain data and analytical findings to non-technical audiences.
- Proactive, structured, detail-oriented, and able to work independently.
- Strong English communication skills (both written and verbal) to collaborate effectively with cross-functional teams and stakeholders.
