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.

Experience
5-7 yrs