Data Quality Analyst (Data Engineering)

BGL Group
London
8 months ago
Applications closed

Related Jobs

View all jobs

Rail Asset Data Analyst | Quality & Insights Lead

Senior Data Analyst – Data Quality & Insights (Hybrid, Glasgow)

Python Data Analyst - Quality & Modeling Support

Senior Data Analyst: Data Quality & Insights

Junior Client Data Analyst — Data Quality & Automation

Hybrid Master Data Analyst: Data Quality & Insights

Job Description - Data Quality Analyst (Data Engineering) (006169)

Description

Our purpose is to make great financial decision making a breeze for everyone, and that purpose drives us every day.
It’s why we’re on a mission to create an automated quoting engine, with the simplest of experiences, wrapped in a brand everyone loves!
We change lives by making it simple to switch and save money and that’s why good things happen when you meerkat.

We’d love you to be part of our journey.
Are you passionate about data quality and skilled in bridging the gap between data engineers and business intelligence (BI) engineers? Do you have solid experience in SQL and AWS Redshift? If so, we have the perfect role for you!

Everyone is welcome.
We have a culture of creativity. We approach our work passionately, improve constantly and celebrate our wins at every turn. We are an inclusive workplace and our employees are comfortable bringing their authentic, whole selves to work. This means we’re excited to hear from people with a range of skills, experiences and ideas. We don’t expect you to tick all the boxes, but would love to hear what makes you great for this role.

Some of the great things you’ll be doing:

  1. Collaborating with data engineers and BI engineers to ensure seamless data integration and reporting workflows.
  2. Developing and implementing data quality assurance processes to uphold high standards of accuracy and reliability across our systems.
  3. Optimizing data pipelines and identifying areas for efficiency improvements in AWS Redshift environments.
  4. Establishing monitoring processes to track data quality metrics and proactively addressing anomalies.
  5. Providing actionable insights to stakeholders by supporting BI teams with clean, well-documented data.

What we’d like to see from you:

  1. Strong proficiency in SQL, with hands-on experience in writing and optimizing complex queries.
  2. Expertise in AWS Redshift, including its architecture, performance tuning, and best practices.
  3. A problem-solving mindset with an analytical approach to data challenges.
  4. Excellent communication skills to work effectively across technical and non-technical teams.
  5. Experience in a data quality or similar role where collaboration with engineering and analytics teams was key.

Our people bring our purpose to life.
We champion a culture of innovation and challenge. We have over 300 tech experts across our teams all using the latest tools and technologies including Docker, Kubernetes, AWS, Kafka, Java, Scala, Python, .Net Core, Node.js and MongoDB.

There’s something for everyone.
We’re a place of opportunity. You’ll have the tools and autonomy to drive your own career, supported by a team of amazingly talented people.
And then there’s our benefits. For us, it’s not just about a competitive salary and hybrid working, we care about what matters to you. From a generous holiday allowance and private healthcare to an electric car scheme and paid development, wellbeing and CSR days, we’ve pretty much got you covered!

#LI-HL1

Primary Location

United Kingdom

Work Locations

London - Shoreditch White Collar Factory 1 Old Street Yard, Shoreditch London EC1Y 8AF

#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.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.