Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Engineer

WRK digital
York
1 day ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Job Title: Lead Data Engineer

Location: York/Manchester/Leeds (3 times a week)

Salary: up to 62,000 + Excellent Benefits

Type: Full-Time, Permanent


WRK Digital is proud to partner exclusively with a well-known, high-profile organisation on a transformative data journey. We areseeking a Lead Data Engineer to join its dynamic Data & Business Intelligence function. This is a pivotal role in a data-driven organisation, where high-quality, well-governed, and accessible data is central to strategic and operational decision-making.


As part of this first-class Data and Analytics team, youll play a key role in shaping how data powers every decision being made. As the Lead Data Platform Engineer, you will lead the design, development, and optimisation of the organisations enterprise data platform and its pipelines, enabling data-driven decision-making and innovation.


You will act as the technical lead for a team of skilled data engineers and align the data engineering strategy with business objectives. This role is pivotal in ensuring the availability, security, and scalability of data systems to support the organisations digital transformation.



Responsibilities:

  • Oversee the design, development, and maintenance of robust, scalable, and secure data pipelines.
  • Implement best practices for data pipelines, modelling, and governance to ensure high-quality, consistent, and compliant data.
  • Lead initiatives to modernise legacy systems, migrate data to the cloud, and optimise data storage and processing costs.
  • Ensure the integrity of datasets within the data lake aligns with logical models and that data is well documented in wikis, data dictionaries, etc.
  • Participate in data engineering sprints, develop data pipelines, and peer-review code.
  • Mentor a high-performing team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
  • Define and execute the organisations data engineering strategy, aligned with overall business objectives and technology roadmaps.
  • Ensure data engineering processes adhere to relevant regulatory standards (e.g. GDPR, HIPAA) and organisational security policies.


About You:

Youre a technical expert who thrives on the importance of clean, scalable data solving problems, driving better decisions, and supporting innovation.

Youll bring:

  • Significant experience in data engineering, including leading or mentoring technical teams.
  • Deep understanding of cloud environments such as Azure, AWS, or Google Cloud Platform, and tools like Synapse, Hadoop, or Snowflake.
  • Hands-on experience with programming languages such as Python, Java, or Scala.
  • Strong knowledge of data architecture, modelling, and governance.
  • A track record of delivering complex data projects from cloud migrations to big data solutions.


Why Join:

An organisation with a proud history and a bold future connecting communities, driving innovation, and enabling sustainable growth. As the company continues it's digital transformation journey, this is an exciting opportunity to shape the future of how data is used across the business.

Join now and play your part in building smarter, data-driven solutions that make a real difference.


This role can be based in either York, Manchester or Leeds with the expectation of being in the office 3 times per week.

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.