Manager Risk Mi & Data Governance

Mastercard
London
2 months ago
Applications closed

Related Jobs

View all jobs

Portfolio Revenue & Debt Data Analyst

Portfolio Revenue & Debt Data Analyst

Portfolio Revenue & Debt Data Analyst - Swindon, Wiltshire

Quantitative Risk Manager

Data Governance and Controls Manager

Data Governance and Controls Manager

Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title And Summary
Manager Risk MI & Data Governance
Overview
This is an exciting opportunity to join the Enterprise Risk Management Team in the Vocalink Limited 2nd Line Risk Management Function.
Vocalink Limited enables the payments of 90% of salaries, 70% of utility bills, most ATM transactions, and every cheque cleared in the UK. The successful candidate will become part of a high functioning team, dedicated to delivering a robust, secure and resilient service to 60+million citizens every day with transactional volume of 11 billion/year and total annual transactional value in excess of GBP6 Trillion.
The appointee will be responsible for ensuring rigor, consistency, and efficiency in Vocalink Limited Risk MI and related Dashboards through:

  • Design of appropriate governance, methodology, processes, and procedures to enable the effective delivery and provide requisite rigour over Risk MI and Dashboards reported internally and externally;
  • Production of quality Risk related MI and Dashboards for management and committee / board reporting;
    and
  • Effective Data Management Governance.

Key Responsibilities

  • Risk MI & Reporting Delivery: Delivery of Risk MI and Dashboards for VLL, including Enterprise-level Risk MI and Dashboards (Top Risks, Risk Evolution, Risk Events, and Risk Appetite Metrics), Risk Outcome Metrics Dashboard, and RPSO Risk MI and Dashboards for RPSO Committees.
  • Dashboard Automation & Continuous Reporting Enhancement: Develop and maintain automated Risk Dashboards (e.G. Power BI) for internal and external reporting, ensuring these correctly report underlying data. Drive Continuous improvement of Risk MI and Dashboards to meet evolving regulatory and customer expectations.
  • Ad-Hoc Risk Analysis: Deliver ad-hoc reporting development and data analysis to provide meaningful and value-add insights to Executive Management and Risk Owners (e.G. Data Quality).
  • Risk Management Compliance Reporting: Develop and produce ‘Policy and Standards Compliance Metrics’ to report compliance and level of embedding across VLL Functions.
  • Reporting & Data Management Governance: Maintain Risk MI and Dashboard Governance, including Reporting and Metric Dictionary, Key Data Elements (KDEs), and Data Lineage Mapping.

All About You (Skills & Experience)

  • Experienced in the development and production of Risk MI and Dashboards, with data analytics experience.
  • Technical knowledge and proficiency in use of PowerBI (or equivalent tooling) to produce Risk MI, reporting, and dashboards and drive automation of reporting.
  • Experience in developing leading and lagging metrics to monitor outcomes (e.G. data quality, policy compliance).
  • Ability to synthesis data into meaningful insights for Management.
  • Communication - Requires effective communication skills – both written and oral - to deal with diverse stakeholder base (internal and external, senior and junior), acting as a bridge as well as guide for the implementation of new capabilities.
  • Decision-Making & Problem Solving - Strong aptitude in decision-making, problem solving, and applying judgement to complex topics.

Desired

  • Knowledge and understanding of Risk Management.
  • Experience of working with Data Lakes.
  • Experience within Critical National Infrastructure responsible organizations.
  • Experience of working with other regulatory bodies (non-UK).

Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

  • Abide by Mastercard’s security policies and practices;
  • Ensure the confidentiality and integrity of the information being accessed;
  • Report any suspected information security violation or breach, and
  • Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.