Head of Data Engineering (Basé à London)

Jobleads
Greater London
6 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering - London

Head of Data Engineering

Head of Data Engineering

Head of Data Engineering

Head of Data Engineering – Azure Lakehouse Lead

Head of Data Engineering (Grade M1)

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.

Careers Jobs

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

🍪 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.