Senior Data Analytics

• Lead end-to-end delivery of strategic Markets data analytics programmes, from solution design and development through testing, UAT, and final sign-off.
• Run simultaneous projects, manage delivery timescales and communicate effectively; translate complex delivery schedules into JIRA plans and Confluence documentation.
• Design and build a best-in-class data analytics platform that surfaces actionable insights into root causes of data quality issues.
• Analyse end-to-end data flows across Markets domains to identify control gaps; own the design of data specifications and integrated dashboard analytics in partnership with Technology.
• Deploy machine learning models and advanced SQL and Python solutions to detect data quality anomalies at scale.
• Design and continuously enhance Control Analytics across Critical Data Elements and associated Data Quality validations.
• Mentor and develop team members, including VPs and junior analysts, coordinating workflows and managing competing priorities.
• Prepare and present executive-level updates for senior management, governance forums, Internal Audit, and regulators.

Key requirements:

• 12+ years in financial services, with depth in Markets data, data analytics, data transformation, risk management, or data governance.
• Expert-level SQL, solving complex large-scale data problems in trading or Markets environments.
• Solid command of Python ecosystem — Pandas, NumPy, Jupyter notebooks.
• Advanced Tableau skills for high-impact dashboards for senior stakeholders.
• Deep knowledge of Markets products, trade lifecycle, data structures across Equities, Rates, FX, Spread Products, Commodities.
• Strong risk data expertise: historical market data, data governance, model input quality for market risk (VaR) and counterparty credit risk.
• Excellent written and verbal communication, ability to influence senior stakeholders.

Nice to have:

• Hands-on AI/ML initiatives, including large language models (ChatGPT, Claude, Gemini) or scikit-learn.
• Dimensional data modelling for analytical warehouses and large-scale data manipulation.
• Translating delivery schedules into JIRA and Confluence plans.
• Master's in quantitative, technical, or business discipline.