At a Glance
- Tasks: Design and build scalable data solutions using Snowflake and DBT.
- Company: Leading financial services organisation driving data transformation.
- Benefits: Competitive salary, career growth, and opportunities to work with cutting-edge technology.
- Other info: Collaborative environment with a focus on modern engineering practices and AI.
- Why this job: Shape the future of investment decision-making and make a real impact.
- Qualifications: Strong experience as a Data Engineer with expertise in Snowflake and data modelling.
The predicted salary is between 59400 - 72600 £ per year.
Reference: JN -072026-494665We're looking for experienced Data Engineers to join a high-profile data transformation programme that will shape the future of investment decision-making and regulatory reporting within a leading financial services organisation.
This is an opportunity to work on a business-critical initiative that will transform how investment and portfolio data is managed, analysed and utilised across the organisation.
You'll be building modern data solutions from the ground up, working alongside senior stakeholders and helping deliver a platform that will have long-term strategic impact.
If you're passionate about solving complex data challenges, enjoy working with investment data, and want to be part of a modern cloud data engineering environment embracing AI-assisted development, we'd love to hear from you.
The Role As a Data Engineer, you'll be responsible for designing, building and optimising scalable data solutions within a modern Snowflake environment.
Working closely with technical teams, business stakeholders and investment specialists, you'll help deliver robust data products that support portfolio analysis, regulatory requirements and business decision-making.
This is a highly collaborative role where technical excellence is equally as important as communication and stakeholder engagement.
Key Responsibilities Design, develop and maintain scalable data pipelines using Snowflake and DBT.
Build high-quality data models, including dimensional and star schema models.
Engineer solutions supporting complex investment and portfolio data.
Work with market data platforms and external data feeds.
Develop solutions for time-series datasets and analytical workloads.
Collaborate with business stakeholders to understand requirements and translate them into technical solutions.
Contribute to the ongoing evolution of the organisation's cloud data platform.
Support the adoption of modern engineering practices, automation and AI-assisted development tools.
Champion best practice around data quality, governance and performance optimisation.
Skills & Experience Essential Strong commercial experience as a Data Engineer.
Expert knowledge of Snowflake.
Strong experience with DBT.
Data modelling experience, including dimensional modelling and star schemas.
Experience working with time-series data.
Experience integrating and managing market data feeds or financial datasets.
Strong SQL and analytical problem-solving skills.
Financial Services experience, ideally within Wealth Management, Asset Management or Investment Management.
Excellent stakeholder management and communication skills.
Highly Desirable Experience working with
Portfolio suitability Portfolio risk Investment analytics Investment data platforms Exposure to modern AI-assisted engineering tools such as Git Hub Copilot, Claude, Snowflake Cortex AI or similar.
Experience within modern cloud-native data platforms and Dev Ops practices.
Git Hub Enterprise or similar source control experience.
What We're Looking For We're looking for engineers who combine deep technical expertise with genuine curiosity.
You'll be someone who: Enjoys solving complex business problems through data.
Is hands-on and enjoys building solutions rather than simply designing them.
Takes ownership and proactively engages with stakeholders.
Can explain technical concepts clearly to both technical and non-technical audiences.
Has a genuine interest in modern engineering practices and AI-powered software development.
Thrives in collaborative, fast-paced delivery environments.
Technology Stack Snowflake DBTSQLGit Hub Enterprise Modern cloud data architecture AI-assisted engineering tooling Market Data Platforms Why Apply?Join a major business transformation programme with significant executive sponsorship.
Work on genuinely greenfield and transformational data solutions.
Influence the design of a modern enterprise data platform.
Collaborate with senior business stakeholders and investment specialists.
Be part of an organisation investing heavily in cloud technologies, modern engineering practices and AI-enabled development.
Excellent opportunities for career growth, technical ownership and long-term impact....
Data Engineer in London employer: Hydrogen
Join a prestigious law firm in London that values excellence and innovation, offering private equity lawyers a dynamic work environment where collaboration and professional growth are at the forefront. With a commitment to employee development, competitive benefits, and a vibrant culture, this firm provides a unique opportunity to thrive in one of the world's leading financial hubs.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Hydrogen!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineer at Hydrogen.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Hydrogen.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Hydrogen, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Hydrogen, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Hydrogen. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Hydrogen
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Hydrogen!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
