Data Engineer

Norton Rose Fulbright
City of London
6 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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.