At a Glance
- Tasks: Design and evolve data architecture to support innovative investment strategies.
- Company: Global investment firm with a commitment to collaboration and individual growth.
- Benefits: Competitive salary, continuous learning opportunities, and a flexible work environment.
- Other info: Join a diverse team dedicated to challenging the status quo and driving change.
- Why this job: Make a real impact on investment decisions using cutting-edge data solutions.
- Qualifications: Experience in data architecture and strong problem-solving skills required.
The predicted salary is between 72000 - 88000 £ per year.
Everything we do exists to find alpha, investment value the market has overlooked. We believe technology is a genuine source of that edge and core to how we find and act on conviction. Because of this, we are modernising our core platform, investing seriously in our data, and building AI solutions that help us achieve alpha for our clients. Data is the thread that connects all of it. Structured, governed and well-understood data is what turns our investment philosophy into decisions, and what makes our platform and AI ambitions possible. We are aligning the platform, data and AI to work as one.
As a Data Architect, you will decide how data is organised and managed across the firm. You will set the standards others build to, map out where our data needs to get to, and help steer the major projects already underway. You will work closely with our investment, business and technology teams – close enough to understand what gives us an edge, and with the authority to make the data decisions that support it.
We are home to world-class talent, and we're looking for more of it to help define what our investment edge looks like next.
Why Orbis?
- Culture. We are committed to our Core Values. We encourage intellectual curiosity, individualism, and collaboration across different areas of the business. We seek to hear our people’s voices – whether quiet or loud. Sharing ideas and challenging the status quo are commonplace.
- Impact. Your work directly supports Orbis’ investment process and decision-making.
- Autonomy and accountability. While guidance and support are provided, team members are responsible for their own work.
- Growth opportunities. We support our people in continuous learning and development.
- Agile environment. We are committed to providing a work environment that balances the needs of our clients, the needs of our teams, and the personal needs, commitments, and interests of our people.
- Philanthropy. Our people can contribute to society in a unique and personal way, through various philanthropy opportunities and programmes.
What will your responsibilities be?
- Define and evolve Orbis’ enterprise data architecture — principles, standards, patterns and target states — and maintain architectural views of key data domains, products, flows and assets.
- Define standards for data modelling, metadata, lineage, master and reference data, and data quality management.
- Provide architectural guidance on how data should be sourced, integrated, stored and consumed across operational and analytical use cases.
- Prepare our data for AI: work with investment and AI teams to identify the data that matters most and the context it needs to be used reliably.
- Work with delivery teams, peer architects and platform engineering so solutions align with our data architecture, governance expectations and wider technology direction.
- Identify opportunities to simplify the data landscape, reduce duplication and improve consistency across platforms, applications and reporting environments.
About You
You are a curious, driven data architect with a growth mindset: you learn continuously, apply what you learn to deliver better outcomes, and can balance strategic direction with practical delivery. You are comfortable working across business, architecture and technology teams, defining standards and target states while influencing stakeholders to apply them sensibly. You build strong working relationships with peer architects, platform engineering and delivery teams, bring structure and clarity to complex environments, and are motivated by improving the quality, coherence and value of data across the organisation.
Skills & Experience:
- Significant experience in data architecture or a closely related role, including as a Data Architect in asset management.
- Exceptional problem-solving skills, with the ability to break down complex, ambiguous problems and turn them into clear, practical solutions.
- Strong understanding of the data landscape and challenges within asset management, including the need to support investment, client, operational, regulatory and enterprise data.
- Strong understanding of enterprise data architecture concepts, including data domains, data products, conceptual and logical modelling, integration patterns, lineage, metadata and lifecycle management.
- Practical experience designing and governing a modern cloud data stack — ideally Snowflake and dbt — including warehouse and database structure, transformation and modelling layers, access and cost governance, and separation of operational and analytical workloads.
- Experience collaborating across business, architecture and technology stakeholders — including enterprise, solution and domain architects, platform and engineering teams — to understand requirements, shape data architecture and align it with wider business and technology direction.
- Strong understanding of data quality management and the practical mechanisms needed to improve trust in data.
- Experience working across complex application, integration and data environments, with the ability to identify opportunities to simplify and improve coherence.
- Ability to produce clear architectural artefacts, principles and standards that guide delivery in a practical way.
- Strong communication, facilitation and influencing skills with senior stakeholders, and the ability to work independently while building strong collaborative relationships across teams.
Instructions for application
To complete your application, please submit your resume.
The Company
We are a global firm with offices across eight countries, over 400 employees and more than $60 billion in assets under management. But those numbers don’t define Orbis. It’s our values, the way we do things day by day, and the value we add for our clients that define us. Our investment philosophy is fundamental, long-term and contrarian. As contrarian investors, we strive to adopt a different perspective, which informs every aspect of our approach. To invest differently, you need to think differently. This is encouraged by having teams of people with different backgrounds, experiences and ways of thinking.
Data Architect in London employer: Orbis Corporation
Orbis is an exceptional employer that fosters a culture of intellectual curiosity and collaboration, empowering employees to take ownership of their projects while providing ample support and guidance. With a strong commitment to continuous learning and development, employees can thrive in an agile environment that balances professional and personal needs. Additionally, Orbis offers unique opportunities for philanthropy, allowing team members to contribute meaningfully to society while working on innovative data architecture solutions.
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We think this is how you could land Data Architect in London
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We think you need these skills to ace Data Architect 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 Orbis Corporation, 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 Orbis Corporation. 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 Orbis Corporation
✨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 Orbis Corporation!
✨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.
