Data Quality Analyst

Hanson Lee
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Quality Analyst

Data Quality Analyst

Data Quality & BI Analyst

Data Governance & Quality Analyst

Data Governance Analyst

Data Analyst - Data Quality & CRM Migration - £40k

Job Description

A successful and established London Market syndicate is seeking a highly motivated and detail-oriented Data Quality Analyst to join their team. The Data Quality Analyst is integral to ensuring the organization's data assets are managed, secured, and utilized to support business decision-making.


As Data Quality Analyst will play a key role in maintaining and driving quality standards, policies, and procedures related to data governance while collaborating with stakeholders across departments to enhance data quality and integrity.


NB: If you do not have experience of working in the insurance sector, you will unfortunately NOT be eligible for this role.


Key Responsibilities:

  • Develop and implement a comprehensive data governance framework
  • Help create, implement, and maintain data governance policies and procedures to ensure the business is compliant with relevant regulatory requirements
  • Work with data owners and data stewards to establish and enforce data quality metrics, monitoring, and improvement processes
  • Identify and appoint data stewards, providing guidance and support
  • Develop and maintain a data catalog, taxonomy, and data lineage documentation
  • Advocate for data quality best practices and drive initiatives to enhance data-driven decision-making
  • Develop and deli...

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.