Lead Data Engineer

Burns Sheehan
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Azure Synapse

Lead Data Engineer - Hadoop - Spark - Python

Lead Data Engineer



  • £90,000-£100,000
  • Central London | Hybrid (3 days on-site)

A leading European intellectual property firm is seeking a "Lead Data Engineer" to join its Technology & Innovation team. The firm partners with some of the world's most innovative and successful organisations, helping to improve lives by supporting creativity, technology, and progress.


Its people are at the heart of everything it does – talented, curious, and collaborative individuals who thrive on solving complex challenges and delivering exceptional results. The firm fosters a friendly, inclusive environment where ideas are shared freely, learning is continuous, and everyone is empowered to be their authentic selves.


The organisation is a proud supporter of initiatives that promote diversity, inclusion, and social mobility, and actively champions wellbeing and community engagement across all areas of the business.


🚀 The Opportunity


Reporting to the Director of Technology & Innovation, the "Lead Data Engineer" will play a pivotal role in shaping the firm's data and analytics landscape. The successful candidate will lead a high‑performing team responsible for designing, building, and optimising scalable, cloud‑based data platforms that drive business intelligence, AI innovation, and data‑informed decision‑making across the firm.


This senior role combines hands‑on technical expertise with strategic leadership, offering the opportunity to define the data vision and enable meaningful business transformation.


Key Responsibilities

  • Lead the design and implementation of a modern cloud data platform (Azure, AWS, or GCP).
  • Develop ETL/ELT pipelines to manage structured and unstructured data at scale.
  • Enable self‑service BI and deliver insights through Power BI dashboards and advanced analytics.
  • Integrate AI and automation into the firm's data infrastructure.
  • Establish data governance frameworks, ensuring quality, consistency, and compliance (GDPR, ISO 27001).
  • Partner with business leaders to align data initiatives with organisational objectives.
  • Mentor and develop a team of data engineers and analysts.

Candidate Profile

  • Proven experience leading data engineering or BI teams in complex organisations.
  • Expertise in cloud data platforms and data processing services.
  • Strong skills in Python, SQL, and Power BI (DAX, Power Query, data modelling).
  • Knowledge of ETL/ELT pipelines, data warehousing, and data mesh architectures.
  • Familiarity with AI/ML applications, metadata management, and data lineage tracking.
  • Excellent communication and stakeholder management skills.
  • Degree in Computer Science, Engineering, Mathematics, or a related STEM discipline.

Experience in professional services or legal environments is advantageous but not essential.


Why Join?

The firm believes in maintaining a healthy work‑life balance and offers a culture that supports wellbeing, growth, and flexibility. Alongside a competitive salary, the role offers:



  • 25 days' holiday (increasing with service) plus holiday buy and bonus schemes
  • Up to 10% employer pension contribution
  • Private medical insurance via Bupa
  • Generous family, fertility, and wellbeing policies
  • Hybrid and flexible working arrangements
  • Paid volunteering day each year
  • Access to 24/7 wellbeing and mental health support

To find out more click apply or email


Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.


#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.