Data Analyst

We are a high-growth Fintech company dedicated to expanding financial inclusion through mobile-first digital solutions. By leveraging alternative data streams and real-time processing, we provide fast, flexible, and accessible financial tools to underserved markets globally.
 

Our Mission for This Role: In high-frequency Fintech, every transactional data point tells a story. You will analyze borrower behavior, optimize our scoring data pipelines, and directly influence the algorithms that manage financial risk and fuel our growth.
 

Key Responsibilities

• Risk & Credit Analysis: Analyze transactional patterns, repayment behavior, and user demographics to help optimize credit risk models and minimize default rates.
• Funnel Optimization: Deep-dive into product analytics to identify friction points in the digital application and onboarding funnel, improving conversion rates.
• Dashboard Architecture: Build and automate real-time dashboards (using Tableau, Power BI, or Looker) to monitor daily disbursement volumes, collection rates, and portfolio health.
• SQL Querying & Pipeline Health: Write and optimize complex SQL queries to pull multi-million row datasets from cloud data warehouses (e.g., Snowflake, BigQuery) without slowing down production environments.
• Regulatory & Compliance Reporting: Partner with legal and compliance teams to extract accurate data for mandatory reporting and audit trails.
   

Skills & Qualifications

Required (Day-One Essentials)

• Experience: 2–4 years of experience analyzing data within a Fintech, digital banking, or fast-paced transactional consumer app environment.
• Advanced SQL: Absolute comfort with window functions, subqueries, and blending data from unstructured or semi-structured sources (JSON, nested arrays).
• BI Visualization: Proven track record of building production-grade dashboards that non-technical operations teams can use daily.
• Analytical Grounding: Strong understanding of cohort analysis, customer lifetime value (LTV), customer acquisition cost (CAC), and basic probability.
   

Preferred (The "Nice-to-Haves")

• Scripting: Basic Python (Pandas, NumPy) for automating repetitive data-cleaning tasks.
• Fintech Domain Knowledge: Prior exposure to fraud detection patterns, collection metrics, or alternative credit scoring methods.
• Agile Tooling: Experience with dbt, Git workflow, or handling event-driven streaming data (e.g., Mixpanel, Amplitude).