At a Glance
- Tasks: Build reliable data infrastructure and manage complex data workflows.
- Company: Join a dynamic data and analytics team tackling exciting challenges with cutting-edge technology.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Candidates must be UK-based; apply now to take the next step in your career!
- Why this job: Be part of a supportive environment where your skills can make a real impact.
- Qualifications: 3 years of data engineering experience and strong cloud engineering skills required.
The predicted salary is between 39000 - 65000 £ per year.
Location: London (Hybrid - 4 days on-site)
The Role
We are seeking a mid-level Data Engineer to join a small, high-impact technology team. This role is suited to a self-starter who enjoys autonomy, takes full ownership of their work, and is comfortable operating close to the business in a regulated financial context.
You will design, build, and maintain robust batch data pipelines, transforming data from diverse sources into a consistent, well-modelled SQL-based warehouse. Your work will directly support investment analysis, risk management, accounting, and external reporting.
This is a hands‑on role with real responsibility from day one, offering the opportunity to own projects end-to-end and contribute meaningfully to the firm’s data foundation.
Key Responsibilities
- Build, own, and maintain reliable batch data ingestion pipelines from a wide range of internal and external data sources
- Design, implement, and extend transformation workflows using SQL, dbt, and Python
- Develop clean, consistent, and well‑documented data models to support investment, risk, accounting, and reporting use cases
- Implement data quality, validation, reconciliation, monitoring, and alerting processes to ensure trust and auditability
- Support the production of critical reports used internally and externally, including investor and regulatory reportingWork closely with investment, risk, and finance stakeholders to clarify requirements and translate business needs into robust data solutions
- Respond to ad‑hoc data requests with speed, accuracy, and strong business context
- Take full ownership of deliverables across the full lifecycle: design, build, test, deploy, and maintain
Tech Stack
SQL Server, dbt, Python, Docker, Power BI, private cloud infrastructure (public cloud experience beneficial)
Required Skills & Experience
- 4+ years’ experience in a data engineering, software engineering, or similar technical role
- Experience working in financial services
- Strong hands‑on SQL experience (SQL Server preferred) and experience using dbt
- Solid Python skills for data pipelines, tooling, and automation
- Proven experience designing and maintaining production‑grade data models and pipelines
- Practical understanding of data quality, observability, reconciliation, and audit controls
- Track record of owning work end‑to‑end with minimal supervision
- Exposure to investment, risk, accounting, or reporting workflows within asset management or finance (preferred)
How You Work
- Self‑motivated and comfortable operating with a high degree of autonomy
- Curious and analytical, with a willingness to dig into messy, real‑world data
- Detail‑oriented where controls and accuracy matter, but pragmatic and delivery‑focused
- Confident communicator, able to work effectively with non‑technical but quantitative stakeholders
What We Offer
- Competitive salary of £70,000 – £95,000 depending on experience
- Medical insurance
- High‑ownership role within a small, collaborative team
- Direct exposure to investment, risk, and finance stakeholders
- Opportunity to shape and own core data infrastructure in a growing business
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Data Engineer employer: Oscar
Oscar is an exceptional employer, offering a dynamic work environment in Surrey where innovation meets collaboration. With a strong focus on employee growth, you will have access to professional development opportunities and a generous benefits package, including 25 days of leave and a pension scheme, all while enjoying the flexibility of a hybrid working model.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨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 Oscar!
✨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 Oscar.
✨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 Oscar.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer at Oscar, 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
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 Oscar, 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 Oscar. 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 Oscar
✨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 Oscar!
✨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.
