Data Governance Manager

DBS Bank
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Governance Manager

Data Governance Manager

Data Governance Manager

Data Governance manager

Data Governance Manager

Data Governance Manager

DBS Bank City Of London, England, United Kingdom


Data Governance Manager

DBS is a leading financial services group in Asia, committed to making banking joyful. We are constantly innovating and evolving to create the future of banking. Join us and be part of a team that's shaping the digital landscape of finance.


The Opportunity

Are you an experienced data protection and privacy expert looking to make a significant impact? We are seeking a highly skilled Privacy / Data Governance Specialist to act as our Subject Matter Expert on UK and EU data protection and privacy regulations for the DBS London Branch. This role offers excellent prospects for conversion from a contractor assignment to a permanent position for the right candidate.


Key Responsibilities

  • Advise the DBS London LCS Head and other key London and Head Office stakeholders on implementing UK and EU GDPR within DBS London and Head Office, particularly in the context of expanding our UK private banking business.
  • Develop a working knowledge of Singapore privacy regulations and effectively liaise with Group Privacy teams in Singapore.
  • Acquire knowledge of other relevant privacy regimes (e.g., Switzerland) as future expansion plans evolve.
  • Build strong relationships with key stakeholders in the UK and Singapore by demonstrating expert subject knowledge and a proactive approach to identifying and implementing proportionate and risk-based solutions.
  • Lead the implementation of data governance initiatives.
  • Manage day-to-day compliance with UK and EU privacy regimes within the DBS London business, reporting to the DBS London LCS Head.

Qualifications & Experience

  • Minimum of 5 years' practical experience working closely with specialist Legal, Compliance, Operations, and Technology teams on GDPR-driven transformation projects, ideally with a significant cross-border element.
  • Detailed knowledge of good practices under UK and EU GDPR and privacy regulations, along with applicable ICO guidance.
  • Familiarity with the Singapore PDPA regime and preferably practical experience of the interaction between GDPR and PDPA.
  • Formal legal qualifications/experience are desirable but not essential.
  • You must have the right to work in the UK and do not require sponsorship now or in the future.

Seniority level

  • Mid-Senior level

Employment type

  • Contract

Job function

  • General Business

Industries

  • Banking and Financial Services

Referrals increase your chances of interviewing at DBS Bank by 2x


Get notified about new Data Governance Manager jobs in City Of London, England, United Kingdom.


#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.

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.