Head of Data, CDO, Data Governance, Professional Services, City London

Finsbury Square

1 month ago

Applications closed

Related Jobs

View all jobs

Lead Data/Head of Data Engineer

Data Engineer (18 Months FTC)

Data Governance Analyst

Lead Data Engineer

Data Engineer

Lead Data Architect | Snowflake & AWS | £130k | Roadmap to Head of Engineering

Head of Data, Professional Services, Governance, Compliance, CDO, City of London

Head of Data / CDO required to work for a Professional Services firm based in the City of London. However, this is a hybrid role where you will be expected to be in the office circa 3 days per week.

This is a senior leadership role reporting into the Chief Technology Officer, with full ownership of the firm’s data agenda and the responsibility to deliver meaningful change across the organisation.

What the role involves:

Lead the development, implementation and long‑term embedding of the firm’s data strategy
Turn data into a strategic business asset rather than a back‑office function
Build and run data governance, chair the Data Council and attend key risk committees
Drive data quality, integration, accuracy and consistency across global systems
Oversee data restructuring, cleansing and preparation for better reporting and insight
Deliver and embed policies across retention, usage, classification and compliance
Support automation and artificial intelligence initiatives by building the right foundations
Implement a data platform such as Microsoft Fabric as the firm’s single source of truth
Lead initial programmes including new knowledge management and records management systems
Influence senior stakeholders across multiple regions and build firm‑wide understanding of the value of data
Work closely with Risk and Compliance to manage data risk globally
Shape and support long‑term technology strategy alongside the Chief Technology Officer

Experience required:
Senior data leadership experience within a legal or comparable professional services environment
Confident working with Partners, senior stakeholders and C‑suite leaders
Proven ability to deliver complex change across governance, quality, integration and data programmes
Strong communication skills with the ability to explain complex topics clearly and practically
Solid commercial judgement and a delivery‑focused mindset
Experience building data culture, continuous improvement and governance frameworks
Understanding of modern data platforms, architectures and approaches
Experience with automation or artificial intelligence programmes is an advantage
Data protection qualifications such as CIPP/E or CIPM are helpful but not essential
Comfortable handling complexity, managing competing priorities and standing your ground when needed

This is a major appointment for the firm and they are looking for someone credible, steady, resilient and able to influence senior stakeholders. If you want to take real ownership of a global data agenda and build something meaningful, this will suit you.

It is a great opportunity and salary is dependent upon experience. Please apply now for more details

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

Industry Insights

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

Feb 10, 2026

Careers

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

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

Feb 3, 2026

Jobs

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

Jan 23, 2026

Education

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.