Data Engineer

Norton Rose Fulbright
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Join to apply for the Data Engineer role at Norton Rose Fulbright

Practice Group / Department: Integrations/Development & Data Management

Job Description
We're Norton Rose Fulbright - a global law firm with over 50 offices and 7,000 employees worldwide. We provide the world’s preeminent corporations and financial institutions with a full business law service. At Norton Rose Fulbright, our strategy and our culture are closely entwined. We know that our expansion will mean little unless it is underpinned by truly global collaboration and we understand that pioneering work only takes place when our people have room to move and think beyond boundaries. As well as the relevant skills and experience, we're looking for people who are innovative, commercial and value the work that they do.

We are embarking on an exciting Data Programme of work and are looking for a talented Data Engineer to join our team. You will play a crucial role in building and managing data pipelines that enable efficient and reliable data integration, transformation and delivery for all data users across the EMEA Region.

12 month FTC, possibility of being made permanent

Responsibilities
  • Design and develop data pipelines that extract data from various sources, transform it into the desired format, and load it into the appropriate data storage systems
  • Implement data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data
  • Optimise data pipelines and data processing workflows for performance, scalability, and efficiency
  • Take authority, responsibility, and accountability for exploring the value of information available and of the analytics used to provide insights for decision making
  • Work across the business to establish the vision for managing data as a business asset
  • Monitor and tune data systems, identifies and resolves performance bottlenecks, and implements caching and indexing strategies to enhance query performance
  • Establish the governance of data and algorithms used for analysis and analytical decision making
  • Collaborate with Subject Matter Experts to optimize models and algorithms for data quality, security, and governance
Requirements
  • Ideally degree educated in computer science, data analysis or similar
  • Strategic and operational decision-making skills
  • Ability and attitude towards investigating and sharing new technologies
  • Ability to work within a team and share knowledge
  • Ability to collaborate within and across teams of different technical knowledge to support delivery to end users
  • Problem-solving skills, including debugging skills, and the ability to recognize and solve repetitive problems and root cause analysis
  • Ability to describe business use cases, data sources, management concepts, and analytical approaches
Experience / Skills
  • Experience in data management disciplines, including data integration, modeling, optimisation, data quality and Master Data Management
  • Excellent business acumen and interpersonal skills; able to work across business lines at all levels to influence and effect change to achieve common goals.
  • Proficiency in the design and implementation of modern data architectures (ideally Azure / Microsoft Fabric / Data Factory) and modern data warehouse technologies (Databricks, Snowflake)
  • Experience with database technologies such as RDBMS (SQL Server, Oracle) or NoSQL (MongoDB)
  • Knowledge in Apache technologies such as Spark, Kafka and Airflow to build scalable and efficient data pipelines
  • Ability to design, build, and deploy data solutions that explore, capture, transform, and utilize data to support AI, ML, and BI
  • Proficiency in data science languages / tools such as R, Python, SAS
  • Awareness of ITIL (Incident, Change, Problem management)
Diversity, Equity and Inclusion

To attract the best people, we strive to create a diverse and inclusive environment where everyone can bring their whole selves to work, have a sense of belonging, and realize their full career potential.

Our new enabled work model allows our people to have more flexibility in the way they choose to work from both the office and a remote location, while continuing to deliver the highest standards of service. We offer a range of family friendly and inclusive employment policies and provide access to programmes and services aimed at nurturing our people’s health and overall wellbeing.

We are proud to be an equal opportunities employer and encourage applications from individuals who can complement our existing teams. We strive to create an inclusive and accessible recruitment process for all candidates. If you require any tailored adjustments or accommodations, please let us know.


#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.