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