We are partnering with banks that are embracing changes toward digital banking. These changes are exciting but complex, and we are looking for team members who are motivated by big challenges and are ready to dive headlong into helping us grapple with complexity, build solutions and turn our visions into a reality.We’re looking for a highly analytical Senior Financial Analyst to own end- to- end data handling, deliver rapid ad- hoc analyses, and keep our sophisticated Python- based financial models up to date. You’ll bridge raw data and strategic insights—working across acquisition, customer analytics, pricing, reconciliation, and revenue reporting—while automating processes and applying AI to boost our team’s productivity.
ResponsibilitiesData Extraction & ETL:- Import and onboard data from multiple sources (mail, Jira,...)- Clean, transform and load datasets into datamart for downstream analysis- Maintain and optimize ETL pipelines using Python/SQL and data- engineering best practicesFinancial Reconciliation & Reporting:- Reconcile incentive schemes (acquisitions, CVP, portfolio campaigns)- Prepare billing summaries- Produce monthly/quarterly P&L updates, forecasting packages, and variance analyses- Automate repetitive tasks (report generation, data imports, reconciliation logic) to accelerate decision- makingAd- Hoc & Recurring Analysis:- Prepare and present financial reports, forecasts, and analyses related to credit card partnerships to senior management and stakeholders.- Respond swiftly to stakeholder requests for one- off analyses—such as pricing sensitivity, campaign ROI, customer cohort performance, or what- if scenario evaluations—that support tactical and strategic initiatives.Python- Based Modeling & Automation:- Build, maintain, and frequently update complex financial models in Python to project revenue, expenses, cash flows, and key performance indicators under various scenarios.- Collaborate with data engineering to scale and productionize analytical workflowsCross- Functional Collaboration & Operations:- Liaise with Customer Service on inquiries and with partners on data follow- ups- Communicate insights clearly to non- technical stakeholders; document processes and results- Support “always- on” operational needs in process management, ensuring SLAs are metAI- Driven Productivity:- Research, prototype, and deploy AI/ML tools (e.g., generative AI, predictive analytics) to automate routine finance operations and enhance analytical insight generation- Integrate AI assistants into reporting and model- updating workflows to reduce manual effort and improve accuracy
