Senior Data Analyst - Fintech

Team: Data/AI/ML

Location: Cairo, Egypt, Alexandria, Egypt

Commitment: Full-time

Workplace Type: hybrid

About the team

The Data Analytics team is part of Yassir’s central Data & AI domain, which is responsible for all data related activities in the organization - from Data Engineering, Data Platform management and Data Analytics all the way to AI solutions and MLOps infrastructure development. We are a global team of Data and AI / ML experts, encompassing diverse backgrounds and skills. We strive to provide top-quality Analytics and BI insights to the business, being a proactive partner to our stakeholders, and ultimately to build a mature Data Culture supported by widespread data fluency, responsible and democratized data access, data-driven decision making and automation. We push the envelope of what’s feasible, we challenge each other, we support each other, and we take great pride in our work. Most of all we are value-driven to create impact from our data solutions. If you have an inherent curiosity about the fields Data and AI, a relevant background and an internal zeal and drive to contribute, please come join us!

Responsibilities

• Analyze customer, transaction, merchant, and agent data to identify trends, opportunities, and performance drivers across Wallets, Payments, Bill Payments, Merchant Payments, P2P Transfers, and other Financial Services products.

• Partner with Product Managers, Operations, Risk, Finance, and Engineering teams to answer business questions through data and support product decision-making.

• Develop and maintain dashboards, reports, and KPI tracking frameworks to monitor customer growth, engagement, retention, transaction performance, and revenue metrics.

• Conduct deep-dive analyses on customer behavior, payment funnels, wallet activation, transaction success rates, merchant activity, and agent performance.

• Support product discovery and feature development by identifying customer pain points, behavioral patterns, and growth opportunities using data.

• Design, execute, and evaluate A/B tests, feature launches, promotional campaigns, and product experiments to measure impact and guide future improvements.

• Work closely with Data Science and Risk teams to support customer segmentation, fraud monitoring, credit risk analysis, and predictive modeling initiatives.

• Perform exploratory data analysis (EDA) to uncover actionable insights and support the development of data-driven product enhancements.

• Monitor data quality, identify anomalies, and collaborate with Data Engineering teams to improve tracking, instrumentation, and reporting accuracy.

• Communicate findings and recommendations clearly to both technical and non-technical stakeholders through presentations, reports, and dashboards.

Requirments

Essential

• 2–5 years of experience in Product Analytics, Business Analytics, Data Analytics, or a related analytical role.

• Strong SQL skills and practical experience working with large-scale customer and transaction datasets.

• Proficiency in Python for data analysis, statistical modeling, and data visualization.

• Experience working with digital products, preferably within Fintech, Payments, Wallets, Banking, Lending, or Financial Services.

• Solid understanding of statistics, hypothesis testing, experimentation, and data-driven decision-making.

• Experience building dashboards and reports using BI tools such as Looker Studio, Looker, Tableau, or Power BI.

• Ability to translate business questions into analytical approaches and communicate insights effectively to stakeholders.

• Familiarity with cloud-based data platforms, preferably Google Cloud Platform (GCP) and BigQuery.

• Strong problem-solving, analytical thinking, and attention to detail.

• Excellent written and verbal communication skills in English.

Nice to have

• Experience working with Wallets, Bill Payments, Merchant Payments, P2P Transfers, Agent Networks, Lending, or Credit products.

• Familiarity with fraud analytics, risk monitoring, customer lifecycle analysis, or fintech growth metrics.

• Exposure to machine learning, customer segmentation, or predictive analytics use cases.

Join the ride!