Head of Data Engineering (Basé à London)

Jobleads
Greater London
7 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Group Head of Data - Enterprise Data Strategy - Microsoft Fabric - Permanent - London

Get AI-powered advice on this job and more exclusive features.

Fintellect Recruitment provided pay range

This range is provided by Fintellect Recruitment. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Direct message the job poster from Fintellect Recruitment

Director | Headhunter | Invoice Finance & ABL | Venture Debt & Growth Cap | Women in Business

Our Client

A new UK-based financial services provider is launching a credit card offering aimed at delivering fair, flexible, and user-friendly financial products to consumers. The organisation is committed to empowering individuals by enhancing their understanding and control of personal finance through clarity, intelligence, and technology.

Its products are designed to simplify money management, giving customers more financial control and flexibility. The goal is to enable smarter financial decisions so people can focus more on the positive aspects of life, rather than the stresses associated with managing money.

Responsibilities

AsHead of Data, you will lead the development and implementation of the company’s data strategy. You’ll be responsible for ensuring stakeholders have access to accurate and timely data, working closely with a Data Product Owner to align data initiatives with business objectives.

  • Define and communicate the organization’s data strategy, ensuring alignment with key functions such as risk, finance, product, and customer service.
  • Promote data literacy across the organization by enabling self-service capabilities for non-technical teams.
  • Lead the design and rollout of a scalable data warehouse to support analytics and reporting.
  • Translate business requirements into technical solutions in partnership with the Data Product Owner.
  • Influence the evolution of the loan management system to reflect data strategy goals.
  • Oversee updates to systems and processes, including the creation of robust datamarts for operational use.
  • Manage the development and maintenance of data models, pipelines, and warehousing infrastructure.
  • Ensure high standards of data quality, security, and consistency.
  • Collaborate with engineering teams to define and track key performance indicators.
  • Stay informed on emerging industry trends and technologies.
  • Build and manage a high-performing team of data engineers and analysts.
  • Ensure adherence to regulatory standards such as FCA compliance and GDPR.
  • Lead the data governance forum and contribute to best practices in data management.

About You

Ways of Working

  • Comfortable in a fast-paced, evolving environment.
  • Focused on automating repetitive tasks to drive efficiency.
  • Implements rigorous checks to ensure data integrity.
  • Hands-on and ready to assist in technical tasks when needed.

Your Approach

  • Self-driven with a strong curiosity for systems and data.
  • Detail-oriented with a collaborative mindset.
  • Thrives in a startup or scaling environment where adaptability is key.

Your Experience

  • Demonstrated experience in senior roles related to data engineering or data platform development.
  • Proficient in Python and SQL.
  • Familiar with data integration tools and frameworks (e.g., ETL/ELT, streaming technologies).
  • Experience working with cloud infrastructure (e.g., AWS).
  • Strong knowledge of data modeling, warehousing, and big data platforms.
  • Skilled communicator and team collaborator.
  • Background in financial services, especially credit or lending data, is advantageous.
  • Familiarity with platforms like Databricks, Snowflake, or Redshift.

Seniority level

  • Seniority levelNot Applicable

Employment type

  • Employment typeFull-time

Job function

  • Job functionInformation Technology, Management, and Strategy/Planning
  • IndustriesBanking and Financial Services

Referrals increase your chances of interviewing at Fintellect Recruitment by 2x

Sign in to set job alerts for “Head of Engineering” roles.

London, England, United Kingdom 4 weeks ago

London, England, United Kingdom 1 week ago

Vice President of Engineering - ThriveCart

London, England, United Kingdom 3 months ago

VP of Engineering – Platform & Architecture

London Area, United Kingdom $300,000.00-$700,000.00 4 days ago

Global Head of Data Engineering - £250k tc

London, England, United Kingdom 1 week ago

Greater London, England, United Kingdom 2 months ago

London, England, United Kingdom 2 days ago

London, England, United Kingdom 3 weeks ago

Maidstone, England, United Kingdom £60,000.00-£80,000.00 2 weeks ago

London, England, United Kingdom 2 weeks ago

Greater London, England, United Kingdom 1 day ago

London, England, United Kingdom 3 days ago

London, England, United Kingdom 1 month ago

London, England, United Kingdom 1 month ago

London, England, United Kingdom 1 day ago

Greater London, England, United Kingdom 1 week ago

City Of London, England, United Kingdom £140,000.00-£160,000.00 2 weeks ago

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.


#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.

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.