Senior Data Engineer

Leeds
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

FDM is a global business and technology consultancy seeking a Senior Dara Engineer to work for our client within the finance sector. This is initially a 12-month contract with the potential to extend and will be a hybrid role based in Manchester.

Our client is seeking a Senior Data Engineer who will be joining the Prudential & Analytics Platform, a team leading the transformation from legacy on-premise systems to the Google Cloud Platform (GCP). Within this platform, the Analytics Lab is focused on developing strategic data products and enhancing data and analytics capabilities across the business.

This role is ideal for a proactive and curious engineer looking to deepen their technical expertise while contributing to meaningful change. The client follows agile delivery practices, so they’re looking for a self-motivated team member who can take ownership of their work, estimate accurately, and collaborate effectively with cross-functional teams.

Responsibilities

  • Design, develop, and maintain robust data processes to support regulatory and prudential change

  • Lead and support the creation of high-quality data solutions and pipelines for current and future analytics and reporting use cases

  • Champion engineering best practices, including clean code, testing, and documentation

  • Collaborate with Product Owners and stakeholders to understand and refine requirements, ensuring they are translated into actionable backlog items

  • Apply strong analytical thinking and technical judgement to solve complex problems and drive data initiatives forward

  • Help build a culture of continuous learning and improvement within the engineering team

    Requirements

  • Minimum of 5 years’ experience in a Data Engineering role, ideally in the bank sector

  • Strong passion for data and software engineering with a problem-solving mindset

  • Proficiency in technologies such as dbt, SQL, Python, Java, SAS, or other open-source analytics tools

  • Ability to translate complex business requirements into technical solutions

  • Excellent communication and interpersonal skills; able to explain technical concepts to non-technical stakeholders

  • Experience in building and maintaining scalable data pipelines

  • Working knowledge of Google Cloud Platform (GCP) or other cloud platforms

  • Familiarity with Terraform for infrastructure as code

  • Experience with data engineering principles and methodologies, including Agile and Waterfall approaches

  • Understanding of industry-standard data management and analytics solutions

  • Knowledge of financial services data domains, including Credit Risk, Capital, and Impairment processes

    Why join us

  • Career coaching, mentoring and access to upskilling throughout your entire FDM career

  • Assignments with global companies and opportunities to work abroad

  • Opportunity to re-skill and up-skill into new areas, develop non-linear career paths and build a skillset within your field

  • Annual leave, work-place pension and BAYE share scheme

    About FDM

    We are a business and technology consultancy and one of the UK's leading employers, recruiting the brightest talent to become the innovators of tomorrow. We have centres across Europe, North America and Asia-Pacific, and a global workforce of over 3,500 Consultants. FDM has shown exponential growth throughout the years, firmly establishing itself as an award-winning employer and is listed on the FTSE4Good Index.

    Diversity and Inclusion

    FDM Group is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, national origin, age, disability, veteran status or any other status protected by federal, provincial or local laws

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.