At a Glance
- Tasks: Build core financial datasets and design scalable data pipelines for critical business decisions.
- Company: Join Spotify, a leading audio streaming service with a passion for creativity and inclusivity.
- Benefits: Flexible work options, extensive learning opportunities, and generous parental leave.
- Why this job: Make a real impact on financial data that drives strategic decisions across the company.
- Qualifications: Experience in Python, Java, or Scala; strong analytical skills; familiarity with cloud-based data processing.
- Other info: Collaborative environment focused on growth, mentorship, and diverse perspectives.
The predicted salary is between 36000 - 60000 £ per year.
Financial Engineering builds the platforms and tools that power Finance and enable strategic decisions across Spotify. Within this group, the Financial Data Platform team turns messy, fragmented realities into clean, reusable foundations. We partner closely with Finance, Product, and Data teams to shape the financial datasets and abstractions that unlock clarity, trust, and scale across the company. 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.
What You’ll Do
- Build core financial datasets and abstractions that serve as sources of truth for business-critical decisions.
- Design, prototype, and operate scalable data pipelines that process billions of records reliably.
- Apply product thinking to the full data lifecycle - from sourcing to documentation to exposure, with a strong focus on meeting consumer needs.
- Define models and abstractions that simplify complex domains such as Premium, Ads, and Royalties.
- Drive strong engineering and data quality standards that improve trust, reusability, and maintainability.
- Collaborate with engineers, data scientists, finance partners, and business stakeholders to design intuitive data products.
- Contribute to tools and libraries that help other teams build high-quality financial data products at scale.
- Engage in mentorship and feedback practices that build accountability, inclusion, and growth across the team.
Who You Are
- You are comfortable working with Python, Java, or Scala and use them to build reliable, maintainable data pipelines.
- You know how to work with higher-level JVM-based processing frameworks such as Flink, Beam, Dataflow, or Spark.
- You are comfortable navigating ambiguity and moving forward when problem spaces are loosely defined.
- You are experienced in large-scale cloud-based data processing, ideally in Google Cloud Platform.
- You have strong analytical skills and communicate insights clearly across technical and non-technical audiences.
- You care about thoughtful data modeling and treat data as a long-lived product.
- You have a passion for clean, maintainable code and robust pipeline design.
- You are curious about systems and data, and you continually look for opportunities to improve them.
- You are skilled in SQL and familiar with large-scale platforms such as BigQuery.
- You value collaboration and build positive relationships across technical and business domains.
Where You’ll Be
This role is based in London. We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens. At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. 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.
Spotify transformed music listening forever when we launched in 2008. 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.
Extensive learning opportunities, through our dedicated team, GreenHouse. Flexible share incentives letting you choose how you share in our success. Global parental leave, six months off - for all new parents. All The Feels, our employee assistance program and self-care hub. Flexible public holidays, swap days off according to your values and beliefs.
Senior Data Engineer, Financial Data Platform in London employer: Spotify AB
Contact Detail:
Spotify AB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer, Financial Data Platform in London
✨Tip Number 1
Network like a pro! Reach out to current employees at Spotify or in the financial data space on LinkedIn. A friendly chat can give you insider info and might just lead to a referral.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. It’s a great way to demonstrate your expertise in building reliable data pipelines.
✨Tip Number 3
Prepare for the interview by brushing up on your problem-solving skills. Be ready to tackle real-world scenarios that involve ambiguity, as this is key in the data engineering world.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining the team!
We think you need these skills to ace Senior Data Engineer, Financial Data Platform in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Senior Data Engineer role. Highlight your experience with Python, Java, or Scala, and showcase any relevant projects that demonstrate your ability to build reliable data pipelines.
Showcase Your Collaboration Skills: We love teamwork at StudySmarter! In your application, mention instances where you've collaborated with engineers, data scientists, or finance partners. This will show us you can work well across different teams and contribute to building high-quality financial data products.
Emphasise Problem-Solving Abilities: Since the role involves navigating ambiguity, share examples of how you've tackled complex problems in the past. We want to see your analytical skills in action and how you approach challenges in data processing.
Apply Through Our Website: Don't forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved.
How to prepare for a job interview at Spotify AB
✨Know Your Tech Stack
Make sure you’re well-versed in Python, Java, or Scala, as these are crucial for building reliable data pipelines. Brush up on your knowledge of frameworks like Flink, Beam, or Spark, and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples that demonstrate your ability to navigate ambiguity and tackle loosely defined problems. Think about specific challenges you've faced in data engineering and how you approached finding solutions.
✨Emphasise Collaboration
Since this role involves working closely with finance partners and other teams, be ready to share experiences where you’ve successfully collaborated across different domains. Highlight how you build positive relationships and foster teamwork.
✨Demonstrate Your Passion for Data Quality
Discuss your commitment to data quality standards and how you ensure maintainability and reusability in your work. Be prepared to talk about your approach to thoughtful data modelling and how you treat data as a long-lived product.