Data Analyst

About the team

The team turns client engagement data into insights that drive better decisions and behaviour change. Rather than focusing on how many campaigns run or how individual campaigns perform, the team helps the business understand which client journeys are effective, why they work and how they should evolve to deliver business and client outcomes. Analytics, experimentation and continuous measurement move the organisation toward more relevant, client-centric engagement aligned to business plans. 

The questions the team answers range from specific to high-impact: 

• Did this campaign shift client behaviour? 
• Where and how was this campaign successful? 
• Which clients are not reached by any campaigns? 
• How has the volume and profile of newly onboarded clients changed over the last few years? 
• The team is a mix of analysts, analytical engineers and a Data Solutions Analyst — with a strong culture of collaboration, learning and knowledge sharing. The team is actively maturing its ceremonies and processes, which means there's real opportunity to shape how things work. 

Why this role exists

The role exists to unlock value from data — making it accessible and meaningful to stakeholders across the organisation. It transforms data into actionable insights that support strategic decision-making, optimise operations and drive business performance. By identifying trends, patterns and opportunities, it enables informed, data-driven decisions and contributes directly to the organisation's strategic objective of being an insights-driven organisation. As a Data Analyst II, you'll be embedded in a pod, working closely with business stakeholders to surface insights that improve journeys and identify untapped audiences. This is a consultative, generalist role — report building is not the core function. 

What you'll do

• Understand the allocated business domain — participate in relevant meetings and forums, learn the data assets available and surface insights that improve journeys and identify untapped audiences 
• Act as a trusted analytical partner — work with and guide cross-functional teams, including marketing, finance and IT, to understand business needs and deliver data-driven insights 
• Challenge and refine stakeholder questions rather than simply accepting requests as given
• Lead data analysis projects and drive business impact through insights — using advanced tools including SQL, Python, R, Tableau and Power BI — and perform complex data analysis and statistical modelling
• Communicate and present findings and recommendations clearly to non-technical stakeholders
• Apply an understanding of financial products, services, industry trends, regulations and compliance requirements to financial data analysis
• Contribute to a culture of learning, mentoring and knowledge sharing within the team 

What the team is looking for

Dashboard-building alone is not enough. The right candidate will:

• Demonstrate strong business acumen — understanding what a result means for the business, not just the data
• Challenge and refine stakeholder questions before answering them
• Manage deadlines and competing stakeholder requests — knowing when to push back
• Navigate complexity and work effectively under pressure
• Learn new domains quickly — roles are generally rotational across pods, so adaptability matters
• Enjoy understanding and influencing how marketing efforts contribute to the bank's performance
• Mentor and teach others — the team values people who strengthen the team around them
• Be comfortable with ambiguity and bring a growth and experimental mindset

Minimum Experience

• Bachelor degree in an analytics or STEM   or  related field,
• At least 5-8 years in data analysis, ideally in financial services or banking
• Data analysis with a significant portion in financial services or banking
• Leading data analysis projects and driving business impact through insights
• Advanced data analysis tools — SQL, Python, R, Tableau or Power BI
• Complex data analysis and statistical modelling
• Working with and guiding cross-functional teams
• Communicating and presenting findings to non-technical stakeholders
• Understanding of financial products, industry trends, regulations and compliance

Qualifications (Minimum)

Tools and Sills

• The team primarily uses SQL and Power BI, with AI adopted to varying degrees. The team is moving toward all work being reproducible, reusable and version-controlled on Git.
• Advanced SQL — complex query writing, performance optimisation and large dataset management
• Advanced Excel — including macros and VBA
• Advanced visualisations and dashboards — Power BI, Tableau or similar
• Python or R — data manipulation libraries (pandas, numpy) and visualisation libraries (matplotlib, seaborn)
• Strong understanding of statistical methods and their application in financial data analysis
• Predictive analytics techniques and their application in financial services
• Data modelling techniques to structure and organise data effectively
• Risk analysis methods and their application in financial services

Conditions of Employment