Cyber Governance Analyst

Fruition IT
Nottingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Compliance Lead

Data Compliance Lead

Data Compliance Lead

Lead Data Engineer

Data Engineer

Data Engineer

Job title:

Cyber Governance Analyst

Below, you will find a complete breakdown of everything required of potential candidates, as well as how to apply Good luck.Location:

UK (Remote with some travel to UK sites)Salary:

Up to £60,000 + car allowance + packageWhy Apply?This is an exciting opportunity to work for a growing organisation in a critical role at the forefront of cybersecurity governance. You will play a pivotal part in shaping secure operations across multiple companies while collaborating with talented teams. Your work will directly influence the company’s risk posture and compliance with industry standards, offering a chance to make a lasting impact.Cyber Governance Analyst Responsibilities:With day-to-day reporting to the Group Information Security Officer (GISO), you will act as a first line of defence, ensuring the implementation and maintenance of security controls aligned with company policies and standards. Key duties include:Collaborating with IT, legal, and policy teams to create and ensure compliance with industry regulations and company-specific policies.Implementing and maintaining Information Security and Privacy Standards and Frameworks, such as ISO 27001, NIST, and CIS.Reviewing system and data architectures alongside engineering teams and architects, recommending best practices.Assessing vulnerabilities, articulating their impact, and recommending controls and mitigations for current and future systems.Conducting risk assessments and effectively communicating security and risk implications to technical and non-technical stakeholders.Managing and supporting project stakeholder expectations with a flexible, pragmatic approach.Cyber Governance Analyst Requirements:Strong knowledge of cybersecurity frameworks (e.g., ISO 27001, NIST, CIS).Proven experience in a similar role, supporting governance, monitoring controls, and managing risks.Ability to assess and articulate the impact of vulnerabilities and recommend mitigations.Skilled in collaborating with multidisciplinary teams and translating technical information for varied audiences.Strong organisational and communication skills, with a proactive and adaptable mindset.What’s in it for me?This is an excellent opportunity to work across multiple subsidiaries, collaborating with diverse teams to build a secure and resilient environment. You’ll gain exposure to cutting-edge security frameworks and best practices while influencing governance strategies at a high level. Additional benefits include:Car AllowanceCompetitive salary and bonus scheme.Healthcare and wellbeing initiatives.Opportunities for professional development and certification.Remote and hybrid working options for enhanced flexibility.We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, sex, disability, religion/belief, sexual orientation or age.

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.