Associate Data Analytics (Credit)
Fortunize Associate Data Analytics (Credit)
The Data Analyst, will play a key role in enabling data-informed decision-making across our commercial vertical. This position is responsible for developing, maintaining, and optimizing data, reporting tools and dashboards that support loan origination, underwriting, and portfolio management. The ideal candidate is detail-oriented, technically proficient, and highly collaborative.
Requirements
- Design, build, and maintain operational dashboards and recurring reports to track key performance indicators (KPIs) related to loan volume, processing times, approval rates, and servicing metrics.
- Collaborate with operations, credit risk, underwriting, product, and finance teams to identify reporting needs and translate business requirements into efficient data queries and visualizations.
- Ensure data accuracy and integrity across operational reports, proactively identifying and resolving discrepancies or anomalies.
- Analyze trends in application flow, merchant behavior, and advance performance to inform process improvements and resource planning.
- Develop documentation and maintain data dictionaries to support data governance and stakeholder self-service.
- Support ad hoc reporting requests and contribute to cross-functional data initiatives.
Required Experience & Skills:
- Bachelor’s degree in a quantitative field (e.g., Statistics, Business Analytics, Economics, Computer Science, or related discipline).
- 2–4 years of experience in data analysis or business intelligence, ideally in fintech, financial services, or lending
- Strong SQL skills and experience working with relational databases.
- Proficiency with data visualization tools (e.g., Tableau, Power BI, Looker, or similar).
- Excellent attention to detail and commitment to data accuracy.
- Strong communication skills, with the ability to explain technical findings to non-technical audiences.
Qualifications:
- Familiarity with small business lending workflows and key operational metrics.
- Experience with Python or R for data manipulation and analysis.
- Understanding of data governance, KPI definition, and dashboard usability best practices.
- Exposure to cloud-based data environments (e.g., Snowflake, Redshift).