Job Description

Develop efficient SQL queries and reusable datasets to support ad-hoc analysis, dashboards, recurring reporting, and business initiatives.
Build, maintain, and improve dashboards, KPIs, automated ETL/ELT workflows, and analytical solutions supporting Credit Commercial performance.
Analyze customer behavior, credit-product usage, utilization, portfolio trends, product performance, and operational performance, and conduct root-cause analysis to explain significant KPI movements and business trends.
Partner with Commercial and business teams to identify growth opportunities through customer segmentation, product penetration, conversion funnels, product adoption, retention, reactivation, cross-sell, and campaign analysis.
Quantify business opportunities, identify key growth and performance drivers, and translate analytical findings into clear and actionable recommendations.
Perform data validation, reconciliation, anomaly checks, and business-logic verification to ensure analytical outputs are accurate and reliable.
Gather and refine stakeholder requirements, align KPI definitions and business logic, and communicate complex findings clearly to technical and non-technical audiences.
Use approved AI-assisted technologies and automation tools to improve analytical productivity, documentation, and workflow efficiency while validating outputs before business use.

Requirements

1 – 2 years of experience in Business Intelligence, Data Analytics, Analytics Engineering, Data Engineering, Product Analytics, Commercial Analytics, or another relevant data-related role
Strong proficiency in SQL and experience working with large and complex datasets
Hands-on experience with BI and visualization tools such as Tableau, Power BI, Looker, or equivalent
Working proficiency in Python for data analysis, scripting, or automation
Strong analytical, critical-thinking, and structured problem-solving skills with high attention to data accuracy
Ability to translate technical analysis into clear business insights and recommendations

It's Great If You Have

Experience in Credit, lending, BNPL, fintech, digital payments, banking, or e-commerce
Familiarity with customer segmentation, funnel analysis, experimentation, forecasting, or predictive analytics
Exposure to Git, Airflow, Spark/PySpark, BigQuery, Snowflake, Databricks, or similar technologies
Experience working with culturally diverse teams and professional working proficiency in English
Strong interest in AI, automation, and emerging data technologies

Business Intelligence (Monee)