At a Glance
- Tasks: Build scalable data pipelines and core datasets that drive strategic decision-making.
- Company: Join Spotify, the world's leading audio streaming service, in a dynamic financial engineering team.
- Benefits: Enjoy remote work flexibility, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with support for diverse needs and career development.
- Why this job: Shape the future of financial data and make a lasting impact on a global scale.
- Qualifications: Experience with data processing frameworks and strong data modelling skills required.
The predicted salary is between 80000 - 98000 £ per year.
At Spotify, Financial Engineering is building the platform that powers Finance and enables strategic decision-making across the company.
Our mission is to create trusted financial abstractions that make complexity manageable and insight actionable — supporting everything from premium and ads growth to forecasting, experimentation, and global reporting.
As engineers in the Financial Data Platform team, we turn messy, fragmented realities into clean, reusable foundations.
We build core datasets that represent key financial domains like Premium, Ads, and Royalties.
We create libraries and tools that empower others to produce and trust financial data at scale.
We collaborate deeply with Finance, Product, and Data teams to unlock clarity and drive Spotify’s ambitions forward.
We are looking for engineers who are excited to shape the future of financial data at Spotify.
You will apply product thinking to financial data — managing the full lifecycle from sourcing to documentation to exposure.
Together, we advocate for standards, champion quality, and build systems that others can rely on with confidence.
If you thrive on building foundations that have broad, lasting impact, and want to work where financial data truly drives strategy, we’d love to work with you.
Acquire a comprehensive understanding of how financial data supports diverse consumer needs, from Finance to broader business customers.
Build core datasets and financial abstractions that serve as sources of truth for strategic and operational decision-making.
Design, prototype, and build scalable data pipelines that process billions of data points reliably.
Apply product thinking to data: manage the full data product lifecycle from sourcing to documentation and exposition, always prioritizing consumer needs and success.
Advocate for and implement effective data quality, engineering standards, and reusability.
Collaborate closely with engineers, data scientists, finance collaborators, and business teams to build flexible, intuitive data products.
Define data models and abstractions that simplify access to complex financial domains like Premium, Ads, and Royalties.
Contribute to building tools and libraries that enable other teams to build financial data products at scale.
Experienced with Data Processing Frameworks: Skilled with higher-level JVM-based frameworks such as Flink, Beam, Dataflow, or Spark.
Proficient with large-scale data processing in cloud environments, preferably with experience in Google Cloud Platform.
Knowledgeable About Data Modeling: You treat data as a product, with strong data modeling capabilities.
Passionate About Clean Code: Committed to writing high-quality, maintainable code and building robust data pipelines.
Curious and Inquisitive: You have a deep curiosity about data and systems, always seeking to understand and improve them.
Skilled in large-scale data processing: Comfortable working with SQL and platforms like Big Query.
There will be some in person meetings, but still allows for flexibility to work from home.
We have ways to request reasonable accommodations during the interview process and help assist in what you need.
If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators.
Everything we do is driven by our love for music and podcasting.
Today, we are the world’s most popular audio streaming subscription service.
Senior Data Engineer - Financial Services - Remote in Cambridge employer: Spotify
At Spotify, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Senior Product Manager for our design systems, you'll have the opportunity to shape the future of product development while working in a vibrant city like Stockholm, known for its creativity and tech-forward mindset. We offer flexible working arrangements, a commitment to employee growth through continuous learning, and a supportive environment where your contributions directly impact the user experience across our platform.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer - Financial Services - Remote in Cambridge
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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 Spotify.
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We think you need these skills to ace Senior Data Engineer - Financial Services - Remote in Cambridge
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 Spotify, 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 Spotify. 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 Spotify
✨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 Spotify!
✨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.
