Data Strategy & Governance Lead

Grant Thornton UK
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

More than you expected

Grant Thornton UK LLP is part of a global network of independent audit, tax and advisory firms, made up of some 73,000 people in over 150 countries. We're a team of independent thinkers who put quality, inclusion and integrity first. All around the world we bring a different experience to our clients. A better experience. One that delivers the expertise they need in a way that goes beyond. Personal, proactive, and agile. That's Grant Thornton.

Job Description:

NEW GROUND WON’T BREAK ITSELF.

Every day our teams help people in businesses and communities to do what is right and achieve their goals.

The Data Strategy & Governance Lead is a pivotal role within DRD (Data, RegTech and Digital) practice within FS BRS (Financial Business Risk Services). This role is responsible for providing specialized services in data management and data governance, and act as an SME on a wide range of client engagements including designing, and delivery of data standards across the organisations.

The role demands a proactive, self-motivated individual with strong communication skills and the ability to forge and maintain strong relationships. The successful candidate will be instrumental in growing the function and significantly contributing to business revenue and sales.

We’re happy to talk flexible working and consider reduced hours and job shares, we’ll support you to balance your work and life.

A look into the role

As a Data Strategy & Governance Lead within our Data, RegTech and Digital practice, you will:

  • Provide advisory services to clients including designing and implementing data governance frameworks.
  • Provide technical and practical advice and solutions tailored to individual client needs depending on the scale and nature of their business.
  • Define, assure and implement data and AI governance frameworks, including policies, standards, regulatory compliance, data risks and controls, data quality, metadata management, and regulatory compliance frameworks.
  • Implement and refine data management and governance tools to automate data management, enhancing efficiency and accuracy.
  • Drive sales and business development activities primarily within financial services industry, build and maintain strong client relationships, understand client needs, and develop tailored solutions that address those needs.

Knowing you’re right for us

Joining us as a Data Strategy & Governance Lead, the minimum criteria you’ll need is the ability to provide advisory services to clients including designing and implementing data management, data and AI governance frameworks, conduct data and AI maturity assessments, and benchmarking clients against industry best practices. Proven experience in data and AI strategy and data governance in Financial Services within a consultancy or industry. Experience in driving sales and business development activities. It would be great if you had some of the following skills, but don’t worry if you don’t tick every box, we’ll help you develop along the way.

  • A relevant data qualification e.g. CDMC, DAMA, DCAM.
  • Experience with data governance platforms and tools such as Collibra, Informatica, Alation, Ataccama or equivalent.
  • Experience with data software e.g. Snowflake, Databricks, Microsoft Purview, Collibra, Informatica or AWS.
  • In-depth knowledge of global data regulations and standards e.g., GDPR, DORA, BCBS 239, EU AI Act.
  • Thorough understanding of data architecture, data analytics and data management principles, and the ability to incorporate these into client solutions.

Knowing we’re right for you

Embracing uniqueness, the culture at Grant Thornton thrives on the contributions of all our people, we never settle for what is easy, we look beyond to deliver the right thing, for everyone. Building an inclusive culture, where we value difference and respect our colleagues helps our people to perform at the best of their ability and realise their potential.

Our open and accessible culture means you’ll interact with leaders who are interested in you and everything you bring to our firm. The things that set you apart, we value them. That’s why we give you the freedom to bring your whole self to work and pursue your passions inside and outside of work.

Beyond the job

Life is more than work. The things you do, and the people you’re with outside of work matter, that’s why we’re happy to look at flexible working options for all our roles, and we’ll always do our best to keep your work and life in balance.

The impact you can make here will go far beyond your day job. From secondments, to fundraising for local charities, or investing in entrepreneurs in the developing world, you’ll be giving back to society. It’s that drive to do the right thing that runs through our every move, grounded in our firm’s values – purposefully driven, actively curious and candid but kind.

We’re looking for people who want to contribute, spark fresh ideas and go beyond expectations. People who want to be able to proudly do what’s right, for the firm, our clients, our people and themselves.It’s how it should be.

#LI-ME1

#J-18808-Ljbffr

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.