Role Overview
The Data Analyst is responsible for collecting, analyzing, and interpreting data to generate actionable insights that drive business decisions across credit risk, operations, finance, marketing, and customer experience.
The analyst will work closely with cross-functional teams to develop dashboards, monitor key performance indicators (KPIs), identify trends, and support strategic initiatives through data-driven recommendations.
Qualifications
Key Responsibilities
Collect, clean, validate, and analyze large datasets from multiple sources to ensure data accuracy and integrity.
Develop and maintain dashboards, reports, and scorecards to monitor business performance and operational metrics.
Analyze customer behavior, transaction patterns, loan portfolio performance, and repayment trends to provide actionable insights.
Support the Credit Risk team by analyzing default rates, delinquency trends, customer segmentation, and risk indicators.
Monitor product performance and customer engagement metrics to identify opportunities for business growth.
Perform ad hoc analysis to support decision-making across departments, including
Finance, Operations, Sales, Marketing, and Product.
Collaborate with business stakeholders to understand reporting requirements and translate them into meaningful analytical solutions.
Design and automate recurring reports to improve efficiency and reduce manual reporting.
Identify data quality issues and work with relevant teams to resolve inconsistencies.
Develop predictive and descriptive analyses to support business forecasting and strategic planning.
Interpret complex datasets and communicate findings through clear visualizations and presentations.
Track key business KPIs such as customer acquisition, loan disbursement, repayment
performance, portfolio quality, revenue, and operational efficiency.
Assist in A/B testing, campaign performance analysis, and customer retention initiatives.
Ensure compliance with data governance, privacy, and security policies.
Continuously identify opportunities to improve reporting processes and analytical capabilities.
Provide risk insights to support effective collections and recovery strategies.
Key Performance Indicators (KPIs)
The Data Analyst's performance will be measured using, but not limited to, the following KPIs:
Accuracy and timeliness of reports and dashboards.
Data quality and integrity metrics.
Dashboard adoption and stakeholder satisfaction.
Reduction in manual reporting processes through automation.
Quality and impact of business insights delivered.
Support provided to business units in achieving operational and financial targets.
Timely completion of analytical requests.
Improvement in reporting efficiency and decision-making through analytics.
Requirements:
Bachelor's degree in Statistics, Computer Science, or a related quantitative discipline.
2 - 4 years of experience as a Data Analyst, preferably within a FinTech, Financial
Services, Banking, or Technology environment.
Experience working with financial, transactional, or lending datasets is an added advantage.
Advanced Microsoft Excel and SQL skill.
Experience with Power BI, Tableau, or Looker.
Proficiency in Python or R for data analysis.
Strong understanding of relational databases.
Knowledge of ETL processes and data warehousing concepts.
Experience using APIs and integrating multiple data sources is an advantage.
Strong statistical and analytical thinking.
Ability to identify trends, anomalies, and business opportunities.
Excellent problem-solving and critical-thinking skills.
Ability to convert complex data into meaningful business insights.
Excellent communication and presentation skills.
Strong attention to detail and accuracy.
Key Performance Indicators (KPIs)
Accuracy and timeliness of reports and dashboards.
Data quality and integrity metrics.
Dashboard adoption and stakeholder satisfaction.
Reduction in manual reporting processes through automation.
Quality and impact of business insights delivered.
Support provided to business units in achieving operational and financial targets.
Timely completion of analytical requests.
Improvement in reporting efficiency and decision-making through analytics.
Added Advantage
Professional certifications in Data Analytics, Power BI, SQL, Python, or Data Science.
Experience working with cloud data platforms such as AWS, Azure, or Google Cloud.
Familiarity with FinTech products such as digital lending, payments, wallets, or embedded finance.
Knowledge of financial reporting, risk analytics, and regulatory reporting is an added advantage.