Data Analyst

Role purpose

Key responsibilities

• Extract and analyse data from data lakes and other sources to provide actionable business and strategy insights.
• Develop, maintain, and automate BI and management-information reports, ensuring accuracy and relevance.
• Use SQL, Python, Power BI, and other tools to automate reporting and streamline analysis.
• Partner with Product, Commercial, Technology, Customer Services, and Operations to understand requirements and support data-led improvements.
• Develop and track KPIs and consistent measures for company-wide decision-making.
• Use SQL, Snowflake, Power BI, Usabilla, Google Analytics, Optimizely, ContentSquare, and Hotjar for analysis and reporting.
• Apply statistical modelling, segmentation, time-series analysis, and predictive techniques to analyse customer behaviour and potential business impacts.
• Use AI and machine-learning platforms to enhance analytical capability and productivity.
• Analyse data across channels and systems with subject-matter experts.
• Mentor colleagues, uphold data-protection and privacy standards, and help integrate insights into business processes.

Essential skills and experience

• At least four years in a Data Analyst role or similar.
• Strong analytics background, ideally in a commercial or digital product/service context.
• Proficiency in SQL, data-visualisation tools, and dashboard/report creation; examples include Streamlit/Python, Power BI, Data Studio/Looker, and GA4 Reports.
• Experience with Google Marketing Cloud, including Google Tag Manager, Google Analytics, and Google Search Console.
• Knowledge of statistical methods, data modelling and architecture, and data-science techniques including segmentation, time-series, and predictive modelling.
• Experience using AI and machine-learning platforms as a power user.
• Effective communication and presentation, stakeholder management, relationship-building, problem-solving, critical thinking, and attention to detail.
• Ability to manage multiple priorities and deadlines; understanding of KPIs and success measures; experience in data projects.

Desirable experience

• Investment-industry experience or strong interest in financial markets, investment products, and market trends.
• Python or another language for data analysis.
• A/B testing, funnel optimisation, and conversion-rate optimisation.
• Familiarity with machine-learning techniques and predictive modelling.
• Familiarity with Jira, Confluence, or similar project-management tools.

Work arrangement

• Hybrid: three days in Manchester or Leeds and two days remote.

Salary

• Salary starts from £32,000; no pay cadence is stated.

Benefits

• Benefits include pension contributions, life assurance, income protection, private medical insurance, annual leave, investment-product discounts, a wellbeing fund, and retail and flexible benefits.