Data Analyst II - Commercial Analytics

About The Role

Data Analysts at this level are end-to-end owners of how data is leveraged to solve complex business problems within and beyond their immediate area. You will autonomously consult with a broad range of stakeholders—translating ambiguous business needs into technical solutions, setting success metrics, and shaping the way forward through actionable data insights and clear communication.

In this role, you will design, build, and maintain robust PySpark data pipelines and tables, ensuring alignment with established data models and best practices. You will drive the translation of business requirements into scalable, production-ready code, with a particular focus on payment cost analysis and financial data. You will lead data quality initiatives, proactively refine and validate tables, and collaborate with both senior and junior analysts to deliver reliable, long-term data solutions. You will also be responsible for documenting data lineage and technical processes, and for sharing knowledge and best practices across the team and wider analytics community. Your technical and analytical expertise will directly drive continuous improvements in our data infrastructure, ensuring high standards of data quality, scalability, and reliability.

About The Team

You will be a key member of the Fintech department and the Commercial Analytics Team, collaborating across teams and disciplines. As a senior contributor, you will own the design and delivery of PySpark data pipelines and tables, translating complex business requirements into scalable, production-grade solutions. You will work closely with business stakeholders, analysts, and data engineers to deliver insights and solutions that inform critical decisions in payments and financial analysis. You will support junior team members, contribute to the development of best practices, and play an active role in driving the continuous improvement of our data infrastructure to meet evolving business needs.

B.Responsible

• Owns the end-to-end design, implementation, and maintenance of complex PySpark data pipelines and production-ready code, delivering scalable solutions to ambiguous business problems with minimal supervision.
• Proactively translates business requirements into technical specifications, collaborating with a broad range of stakeholders to deliver impactful data solutions.
• Leads data quality initiatives, including root cause analysis, data reconciliation, and systematic validation of key reports, metrics, and workflows.
• Communicates results and recommendations clearly to both technical and non-technical stakeholders, influencing business decisions at the team and department level.
• Drives adoption of new technologies and approaches, continuously improving data infrastructure for scalability, reproducibility, and long-term reliability.
• Actively participates in and leads analytics team activities (standups, planning, retrospectives), and contributes to the broader analytics community through knowledge sharing and recruitment.
• Maintains a deep understanding of the big data ecosystem, and proactively connects with peers across teams and crafts to ensure successful project outcomes.
• Develops significant expertise in the operational, tactical, and strategic workings of the department, with a focus on identifying and executing on business and technical opportunities.
  
  
  

• Educational background in a quantitative field—Master’s degree preferred.
• 5+ years of experience in data analytics, insight generation, and data visualization, with a proven track record of delivering end-to-end big data analytics projects in an industry setting.
• Must have advanced expertise in SQL, PySpark, and Python, with experience designing and maintaining large-scale data pipelines and transformations.
• Demonstrated ability to translate complex business requirements into scalable, production-ready solutions.
• Strong familiarity with big data platforms, version control, and modern data engineering best practices.
• Experience with financial metrics and payments data is a strong plus.
• Ability to balance attention to detail with a strategic, big-picture perspective.