Junior Data Governance Analyst | £35,000 + Bonus & 10% Pension

Bristol
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer (18 Months FTC)

Senior Data Engineer

Junior Data Engineer

Data Engineer

Data Analyst Training Course (Excel, SQL & Power BI)

Junior Data Governance Analyst

Bristol (Hybrid)

Salary - £30,000 to £35,000 + Bonus, Health, 25 days holiday, 10% Pension

Are you early in your data career and interested in Data Governance, but don’t want a heavily technical or coding‑focused role?

This is a fantastic opportunity to build a long‑term career in data governance within the utilities and critical infrastructure sector, where data accuracy, security, and trust genuinely matter.
We’re looking for a Junior Data Governance Analyst with a good understanding of data concepts, strong communication skills, and the right attitude to learn. You don’t need hands‑on data governance experience yet full training and support will be provided.

What you’ll be doing
You’ll help embed good data practices across the organisation by working closely with both technical teams and non‑technical stakeholders. This role sits at the intersection of data, people, and process.

Your responsibilities will include:

Supporting the implementation of data governance frameworks, standards, and policies
Helping maintain data definitions, glossaries, metadata, and data catalogues
Engaging with stakeholders to understand data issues and support improvements
Assisting with data quality assessments and remediation activities
Supporting data classification, access control, and compliance processes
Promoting data literacy and good data behaviours across the business
This role focuses on understanding and improving data, rather than building pipelines or writing code.

What we’re looking for
This role is designed for junior or early‑career professionals who want to grow into data governance.

Essential:

A basic understanding of data concepts (data lifecycle, quality, governance, privacy)
Interest in enterprise data platforms and how data flows through systems
Awareness of cloud data environments, particularly Azure
Strong communication skills and confidence working with stakeholders
A positive attitude, curiosity, and willingness to learn
Desirable (but not required):

Exposure to tools such as Azure Data Factory, Azure SQL, Data Lake, Purview, or Power BI
Understanding of GDPR or data protection principles
Degree or background in data, IT, information management, or a related field
Why this role is a strong career move:

Clear entry point into data governance, a growing and in‑demand discipline
Training provided you’ll learn data governance best practice on the job
Exposure to large‑scale, real‑world data environments without heavy coding
Work in a regulated, purpose‑driven industry where data quality truly matters
Opportunity to build long‑term progression into senior governance or data roles
Who this role is ideal for:

Junior data analysts or data professionals looking to move into governance
Graduates interested in data, risk, compliance, or information management
Individuals who enjoy working with people and improving processes
Anyone looking for a data career that balances technical understanding with communication
If this sounds like something you would be interested in get in touch, (url removed)

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 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.

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.

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.