Manager Business Performance & Analytics

Role Overview

This position leads the team that builds and maintains the analytical infrastructure powering credit decisions, dashboards, and regulatory reporting across the bank. Day to day, you will oversee the development of data services and APIs, productionise machine learning and scoring workflows, and make sure internal stakeholders can trust the numbers they rely on. Your work directly determines whether the bank can respond quickly to market changes, satisfy auditors, and give customers fair and accurate credit outcomes.

Key Responsibilities

• Architect and deliver back-end data services, including RESTful APIs that feed credit scoring engines, performance dashboards, and internal analytics platforms.
• Integrate analytics services with enterprise data warehouses, data lakes, and external vendor systems while maintaining strict version control, code review, and deployment automation standards.
• Partner with data engineers to move experimental models and ad-hoc analyses into robust, repeatable production pipelines with feature stores and clear data lineage.
• Lead a team of back-end developers and analytics engineers, setting quarterly objectives, reviewing performance, and identifying training or mentoring opportunities.
• Define and track KPIs for system uptime, API response latency, pipeline failure rates, data quality scores, and dashboard adoption, then use those metrics to prioritise technical debt and new investment.
• Translate requirements from Risk, Credit, Finance, and Operations into technical deliverables, explaining trade-offs and constraints in plain language for non-technical stakeholders.
• Enforce governance and security controls for all data outputs, especially those used in credit decisioning, ensuring every analytical result can be traced back to its source data and model version.
• Manage vendor relationships and validate that any third-party tools or services align with internal architecture, data privacy policies, and regulatory expectations.

Requirements & Qualifications

• Bachelor's degree in computer science, data science, statistics, mathematics, actuarial science, business analytics, or a closely related field; a master's degree is a plus.
• 5–8 years of hands-on experience in data analytics, data engineering, or back-end software engineering, with at least part of that time spent leading or supervising technical staff.
• Proven ability to build and operate data platforms in a regulated enterprise, ideally within banking or financial services where credit scoring or decisioning systems are involved.
• Strong SQL skills and practical experience with data warehouses, data lakes, and analytics modelling approaches such as Kimball or Data Vault.
• Proficiency in Power BI, Tableau, or equivalent visualisation tools for building executive-facing dashboards and self-service reporting.
• Hands-on cloud experience on AWS, Azure, or Google Cloud, including services for compute, storage, and orchestration.
• Familiarity with large-scale data processing frameworks such as Apache Spark, Kafka, or Hadoop, plus working knowledge of ML model deployment, feature engineering, and model monitoring.
• Solid back-end development skills in Python using Django or FastAPI, with understanding of REST design, authentication patterns, and secure API development.
• Experience with CI/CD pipelines, Git-based version control, automated testing frameworks, and observability tools for logging and monitoring.
• Excellent communication and stakeholder management skills, with the ability to present complex analytics concepts in a straightforward, persuasive manner to senior leadership.
• Demonstrated commitment to data ethics, customer privacy, and regulatory compliance, particularly around credit risk, AML, and data protection requirements.