Business Data Analyst

Description:

Our client is a Fintech with a strong international footprint and a reputation for delivering intelligent data solutions across the financial services sector. With a focus on innovation, cross-functional collaboration, and operational excellence, they are continually investing in their people and platforms to stay ahead of evolving market demands.

Why should you apply?

This opportunity offers the chance to join a high-performing data team at a critical time of platform enhancement and modernisation. The successful candidate will play a central role in developing high-impact datasets and dashboards that support strategic decision-making. You will gain exposure to leading-edge cloud technologies in a role that balances technical depth with business engagement. The company also promotes flexible work arrangements, excellent learning and development pathways, and a highly supportive team environment.

Who should apply?

This role is ideal for an experienced data professional looking to deepen their analytics expertise in a commercially impactful environment:

• At least 3 years of experience in a data analytics or business intelligence role.
• Proficient with data visualisation tools such as Power BI or Tableau.
• Strong SQL skills and exposure to Python, R, or similar.
• Experience working with cloud-based data warehousing tools is advantageous.
• Able to interpret data meaningfully and communicate findings clearly to stakeholders.
• Analytical thinker with strong organisational and prioritisation skills.

Role and Reporting Lines

You will report to a senior data leader within a structured analytics function that collaborates across business units. Core responsibilities will include:

• Data Architecture – Assist in the design and maintenance of core datasets to support analytics needs.
• Visualisation – Create clear, insightful dashboards and performance reports for various teams.
• Insight Delivery – Identify trends and deliver actionable insights that guide business strategies.
• Data Quality – Support the development of data quality frameworks and conduct regular audits.
• Cross-functional Collaboration – Liaise with technology and operations teams to refine data sources and ensure alignment with business goals.
• Best Practice – Contribute to the adoption of industry-leading analytics standards.
• Training – Educate internal users on data literacy and the use of reporting tools.
• Documentation – Maintain structured guidance on processes, governance, and technical configurations.