Data Analytics Senior Specialist

Job description

Overview Responsible for leveraging data, analytics, automation, and AI-enabled tools to deliver high-quality, scalable business solutions. The role acts as a subject matter expert across analytics platforms, driving insight generation, operational efficiency, and data-driven decision-making. This position also plays a key enablement role—upskilling colleagues, promoting self-service analytics, embedding best practices, and serving as a point of escalation for complex analytics, automation, and AI-assisted solutions. Key Accountabilities and main responsibilities Strategic Focus • Understand business needs, gather requirements, and develop bespoke analytics solutions, dashboards and data products aligned to key business objectives. • Identify and implement opportunities to apply AI, prompt engineering and intelligent automation to enhance reporting, forecasting, and decision-making processes. • Develop advanced BI and automation solutions using Alteryx, Power Apps, Fabric, and LLM-enhanced workflows. • Drive uplift in data governance practices, data quality, and metadata management, including adoption of platforms like OneTrust for data cataloguing and retention (GDPR). • Partner with business leaders to identify new use cases where data and AI can improve operational efficiency, customer experience, and risk insight. • Contribute to enterprise strategies on self-service analytics, responsible AI, and democratisation of insight. Operational Management • Build scalable analytical models, simulations, and automated workflows using Alteryx, Microsoft pipelines (Fabric, Power Apps). • Design, develop, validate, and maintain ETL/ELT processes and data integrations aligned to architectural and governance standards. • Improve operational reporting through enhanced quality, reduced lead times, and AI-supported summarisation. • Prompt Engineering for Analytics: • Design and iterate prompts to convert dashboards, SQL output, and tabular data into executive-ready summaries, insights, and recommendations. • Insight Automation: • Use LLMs (Copilot, ChatGPT) to automate recurring reports, detect anomalies, and generate week-over-week insights through structured prompt workflows. • Data-to-Decision Translation: • Tailor AI-generated outputs into stakeholder-specific formats (email briefings, slide bullets, risk callouts) to bridge the gap between raw metrics and decision-makers. • Script Decoding & Logic Translation: • Apply AI tools to translate SQL/Python logic into plain English to support onboarding, knowledge sharing, and cross-functional understanding. • Self-Service BI Expansion: • Build reusable prompt templates into dashboards and BI tools, enabling non-technical users to ask natural-language questions and obtain meaningful insights. • Document systems, data sources, metrics, and processes to ensure transparency, continuity, and auditability. • Provide analytical support in interpreting, reporting, visualising, and presenting insights to stakeholders. People Leadership • Build strong relationships across the business to uplift digital capability, promote adoption of analytics, automation, and AI tools. • Provide coaching, training, and support to business users on reporting tools, dashboards, and safe, effective use of AI copilots and BI features. • Champion a culture of continuous improvement, innovation, and responsible use of AI-augmented analytics. • Role-model MUFG Market Services values and contribute to a high-performing, collaborative environment. Governance & Risk • Define and implement policies, processes, and standards for responsible management of data, including capture, storage, architecture, security, integration, reporting, and analytics. • Embed strong governance controls into analytics and AI-enabled workflows (documentation, lineage, validation, access management, auditability). • Support model and AI risk management requirements to ensure LLM-powered processes are transparent, ethical, and compliant with regulatory frameworks. • Provide guidance on data quality, stewardship, and risk identification, and contribute to development of a unified approach to enterprise dataset management. The above list of key accountabilities is not an exhaustive list and may change from time-to-time based on business needs. Experience & Personal Attributes • Data & Analytics Experience • Extensive hands-on expertise with Alteryx Designer, Alteryx Server, and ideally Alteryx One, with proven ability to design scalable, controlled, and auditable workflows. • Strong skills in Data Engineering, including ETL/ELT development, optimisation, and data pipeline automation using MySQL, Alteryx, and Azure technologies. • Solid understanding of data warehouse architecture, including dimensional modelling, semantic modelling, and performance optimisation. • Experience designing, deploying, and maintaining analytics solutions in Azure, including Azure Synapse Analytics, Data Lake, and Fabric components. • Expertise in building business-facing Power BI dashboards, reports, semantic models, and integrating them with automated workflows through Power Apps • Experience in functional design and delivery of enterprise BI and analytics solutions across complex business environments. • Strong working knowledge of testing methodology, change management and release management, ensuring high-quality, controlled delivery of data products. • Experience in developing and managing enterprise data warehouse environments, including metadata management, lineage documentation, and governance alignment. AI Skills • Experience using AI-assisted tools (e.g., Copilot, ChatGPT) for: • Insight summarisation Logic translation (SQL/Python • plain language) • Prompt-engineered reporting and anomaly detection • Automating insight generation and stakeholder communications • Ability to embed AI features into BI tools to uplift self-service analytics and reduce dependency on speci