Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Engineer - Financial Data Platform

Spotify
Greater London
1 month ago
Create job alert

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 design and operate scalable pipelines that process billions of records. You will apply product thinking to financial data — managing the full lifecycle from sourcing to documentation to exposure. You will define abstractions that simplify complexity and create intuitive paths for our consumers. 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.

What You'll Do

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. Leverage mentorship and constructive feedback to foster accountability, growth, and collaboration within the team.

Who You Are

Experienced with Data Processing Frameworks: Skilled with higher-level JVM-based frameworks such as Flink, Beam, Dataflow, or Spark. Comfortable with Ambiguity: Able to work through loosely defined problems and thrive in autonomous team environments. Skilled in Cloud-based Environments: Proficient with large-scale data processing in cloud environments, preferably with experience in Google Cloud Platform. Strong Analytical Skills: Adept at breaking down complex problems and communicating insights effectively. 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 BigQuery. Excellent Collaborator: You value positive relationships across technical and business domains.

Where You'll Be

This role is based in London, United Kingdom 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.

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

The Future of Data Science Jobs: Careers That Don’t Exist Yet

Data science has rapidly evolved into one of the most important disciplines of the 21st century. Once a niche field combining elements of statistics and computer science, it is now at the heart of decision-making across industries. Businesses, governments, and charities rely on data scientists to uncover insights, forecast trends, and build predictive models that shape strategy. In the UK, data science has become central to economic growth. From the NHS using data to improve patient outcomes to financial institutions modelling risk, the applications are endless. The UK’s thriving tech hubs in London, Cambridge, and Manchester are creating high demand for data talent, with salaries often outpacing other technology roles. Yet despite its current importance, data science is still in its infancy. Advances in artificial intelligence, quantum computing, automation, and ethics will transform what data scientists do. Many of the most vital data science jobs of the next two decades don’t exist yet. This article explores why new careers are emerging, the roles likely to appear, how current jobs will evolve, why the UK is well positioned, and how professionals can prepare now.

Seasonal Hiring Peaks for Data Science Jobs: The Best Months to Apply & Why

The UK's data science sector has matured into one of Europe's most intellectually rewarding and financially attractive technology markets, with roles spanning from junior data analysts to principal data scientists and heads of artificial intelligence. With data science positions commanding salaries from £30,000 for graduate data analysts to £140,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this intellectually stimulating and rapidly evolving field. Unlike traditional analytical roles, data science hiring follows distinct patterns influenced by business intelligence cycles, research funding schedules, and machine learning project timelines. The sector's unique combination of mathematical rigour, business impact requirements, and cutting-edge technology adoption creates predictable hiring windows that strategic professionals can leverage to advance their careers in extracting insights from tomorrow's data. This comprehensive guide explores the optimal timing for data science job applications in the UK, examining how enterprise analytics strategies, academic research cycles, and artificial intelligence initiatives influence recruitment patterns, and why strategic timing can determine whether you join a pioneering AI research team or miss the opportunity to develop the next generation of intelligent systems.

Pre-Employment Checks for Data Science Jobs: DBS, References & Right-to-Work and more Explained

Pre-employment screening in data science reflects the discipline's unique position at the intersection of statistical analysis, machine learning innovation, and strategic business intelligence. Data scientists often have privileged access to comprehensive datasets, proprietary algorithms, and business-critical insights that form the foundation of organisational strategy and competitive positioning. The data science industry operates within complex regulatory frameworks spanning GDPR, sector-specific data protection requirements, and emerging AI governance regulations. Data scientists must demonstrate not only technical competence in statistical modelling and machine learning but also deep understanding of research ethics, data privacy principles, and the societal implications of algorithmic decision-making. Modern data science roles frequently involve analysing personally identifiable information, financial data, healthcare records, and sensitive business intelligence across multiple jurisdictions and regulatory frameworks simultaneously. The combination of analytical privilege, predictive capabilities, and strategic influence makes thorough candidate verification essential for maintaining compliance, security, and public trust in data-driven insights and automated systems.