Business Data Analyst [212743]

Aquent is partnering with a leading global technology company that is revolutionising how individuals and businesses manage their financial lives. This innovative organisation is deeply committed to customer success, leveraging data-driven strategies to enhance experiences and drive operational excellence across diverse international markets. They are at the forefront of integrating AI into their core workflows, transforming how they generate insights and automate processes.

Are you ready to make a significant impact on customer journeys and operational efficiency across global markets? We are seeking a highly motivated and forward-thinking Data Analyst to join a dynamic team dedicated to building cutting-edge data capabilities. In this pivotal role, you won’t just analyse data; you’ll be instrumental in shaping strategy, enabling actionable insights, and establishing robust, AI-ready data foundations that scale across multiple regions. You will be at the heart of improving how this company measures and delivers customer success, directly influencing Customer Experience, Digital Experience, Service Delivery, and Supportability, all while working with groundbreaking AI-native analytics.

This is a 10-month hybrid contract, offering up to £217.09 per day (PAYE). Shortlisted candidates will be contacted by COB Monday, 7th September. Due to the number of applications, we’re unable to get back to each candidate individually.

Key Responsibilities

• Build robust data foundations by collaborating with engineering to develop pipelines, enhance data integrity, and establish clear metric definitions and governance tailored for international markets.
• Develop data capabilities that function consistently across multiple regions and countries, while adapting to specific local requirements (e.g., regulatory, language, or workflow differences).
• Automate recurring reporting and validation processes to boost accuracy and efficiency, ensuring data structures and taxonomies are optimized for downstream AI consumption and model-readiness.
• Create and maintain scalable, user-friendly dashboards that provide comprehensive visibility into key performance metrics, designing reporting that connects metrics across the entire customer journey rather than in isolated silos.
• Deliver deep-dive analytics by conducting root cause analysis to uncover drivers of customer pain points, digital adoption challenges, and operational inefficiencies.
• Translate analytical findings into actionable recommendations that improve customer journeys, optimise staffing and routing decisions, and reduce escalations.
• Advance AI-native analytics by leveraging Generative AI tools to automate recurring insight generation, support AI-powered summarisation and anomaly detection, and identify opportunities to replace manual workflows with intelligent automation.
• Enable experimentation and continuous improvement by supporting A/B testing and digital experience evaluations, analysing performance outcomes, and quantifying customer and business impact.
• Provide baseline metrics, monitor results, and deliver insights to scale successful initiatives, including evaluating the performance and customer impact of AI-assisted and digital experiences.
• Influence strategy through compelling storytelling, communicating insights and trends clearly and persuasively to both technical and non-technical stakeholders to ensure alignment on decisions that enhance customer experience, digital adoption, operational performance, and supportability.

Required Skills & Experience

• Solid experience in Data Analytics or Data Science, with ideal exposure to Customer Success, Operations, or Digital Experience domains.
• Proficiency in SQL for data extraction, transformation, and pipeline development.
• Experience with dashboarding and visualisation tools (e.g., Tableau, Qlik, or similar).
• Familiarity with big data tools (e.g., Snowflake, Databricks, Spark) and ETL processes.
• Proven ability to communicate analytical findings in clear, actionable narratives for both technical and non-technical audiences.
• Strong problem-solving skills with the ability to balance strategic priorities and tactical execution.
• Ability to manage multiple projects simultaneously in a fast-paced environment.
• Proactive and growth-oriented mindset, consistently identifying opportunities for innovation.

Nice-to-Have Skills

• Experience with Python or R for advanced analytics, automation, or experimentation support.
• Knowledge of statistical methods and experimentation (e.g., A/B testing).
• Machine learning and Generative AI experience.
• A genuine enthusiasm for Generative AI and a growth mindset toward how emerging tools can accelerate analytical work, actively curious about how LLMs, AI-native workflows, and intelligent automation can enhance impact.
• Hands-on experience applying Generative AI to analytics workflows (e.g., automated insight generation, AI-assisted analysis, or LLM-enhanced data pipelines).
• Experience working with call centre and/or digital support KPIs.
• BS or MS in an appropriate quantitative field.

Our Client is a global technology platform that specialises in overcoming the world’s most important financial challenges. Their products and services are driven by artificial intelligence, and their accounting software is one of their most recognisable creations. Considered one of the top companies to work for, they are proud of their company culture and entrepreneurial spirit.

Aquent is dedicated to improving inclusivity & is proudly an equal opportunities employer. We encourage applications from under-represented groups & are committed to providing support to applicants with disabilities. We aim to provide reasonable accommodation for any part of the employment process, to those with a medical condition, disability or neurodivergence.

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This is a 10-month hybrid contract, offering up to £217.09 per day (PAYE). Shortlisted candidates will be contacted by COB Monday, 7th September. Due to the number of applications, we’re unable to get back to each candidate individually.

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