Senior Data Analyst

We are augmenting our Data Intelligence team with a Senior Data Analyst who can turn business and portfolio data into clear, decision-ready insight. The role involves deep-dive analysis, dashboarding, and ad-hoc investigation across lending business data, with a domain leaning toward risk, credit, or marketing/cross-sell depending on the candidate's strongest background. This is an individual-contributor analytics role focused on insight generation, not a data engineering or platform-building role.

Responsibilities:

• Conduct deep-dive and ad-hoc analysis to answer business questions from leadership and functional stakeholders.
• Build and maintain recurring dashboards and reports (portfolio performance, campaign / cross-sell tracking, or risk & delinquency trends depending on domain fit).
• Perform root-cause analysis on business or portfolio trends and translate findings into clear, actionable recommendations.
• Partner with Risk, Credit, or Marketing teams (as aligned) to define the metrics and cuts that matter for their decisions.
• Write and optimise SQL queries against large transactional and portfolio datasets.
• Validate data quality and flag inconsistencies back to source/data teams.
• Present findings to business and senior stakeholders in a clear, structured manner.

Requirements:

• 5-7 years of experience in a data analyst/business analytics role in a Bank or NBFC, with lending experience mandatory (microfinance exposure not required).
• Strong hands-on SQL skills and comfort working with large, real-world transactional datasets.
• Experience in at least one of: risk/credit analytics, or marketing/cross-sell analytics.
• Strong analytical and root-cause problem-solving skills, with the ability to move from a business question to a data answer independently.
• Excellent communication skills, able to present analysis and recommendations to business and senior leadership.

Good to Have:

• Experience with BI tools such as Power BI or Tableau.
• Working knowledge of Python or R for analysis.
• Exposure to credit bureau data, collections data, or campaign/CRM data.
• Familiarity with loan lifecycle metrics (delinquency, vintage, roll-rate) or marketing metrics (funnel, attribution, CLV).
• Bachelor's or Master's degree in a quantitative discipline: Statistics, Economics, Engineering, Data Science, or related field.