Data Engineer - Commerce Platform

As a Data Engineer in our commerce platform team at Spotify, your primary responsibilities will include designing and building core data capabilities to identify and prevent misuse across various plans and services. You'll maintain and enhance data pipelines, ensure they comply with SOX regulations, and collaborate closely with product and engineering teams to understand their data requirements, ensuring they align with the existing landscape and technical strategy. Continuous improvement of data systems for efficiency and reliability is also critical in this role.

To qualify for the Data Engineer position at Spotify, candidates should have proven experience in data engineering, with proficiency in creating reliable and scalable data pipelines using tools such as DBT and Scio. Strong SQL skills combined with knowledge of programming languages such as Scala or Python are essential. Experience in maintaining scalable backend systems using Java and familiarity with cloud-based platforms like AWS, GCP, or Azure will set you apart. Strong collaboration and communication skills are also vital when working with both technical and non-technical stakeholders.

At Spotify, we value work-life balance and offer flexibility for our Data Engineers. Whether you're based in London or Stockholm, you have the opportunity to work where you feel most productive, including from home. While there will be some in-person meetings, we prioritize a flexible work arrangement that accommodates both personal and professional commitments, allowing our team members to thrive.

Data Engineers at Spotify leverage a range of advanced technologies and tools to execute their duties efficiently. Key technologies include DBT and Scio for building data pipelines, as well as SQL for data querying. Additionally, a solid understanding of programming languages such as Scala or Python is crucial, and knowledge of cloud-based platforms like AWS, GCP, or Azure is essential for maintaining scalable data systems. Familiarity with A/B testing frameworks is also beneficial for those looking to excel in this role.

The interview process for the Data Engineer position at Spotify involves multiple stages to ensure a thorough assessment of your skills and fit for the team. Typically, candidates will undergo initial screening interviews followed by technical assessments to evaluate their knowledge of data engineering principles, including the creation of data pipelines and analytics across various platforms. There will also be behavioral interviews focusing on collaboration and problem-solving skills, providing an all-encompassing view of how candidates align with Spotify's mission and culture.

In response to this question, highlight specific projects where you've designed and implemented data pipelines using DBT. Discuss the challenges you faced and how you overcame them, such as dealing with data integrity or pipeline optimization. Demonstrate your understanding of DBT's capabilities, such as version control and testing, and provide insights into how your work contributed to greater data reliability and efficiency within your team.

When addressing SOX compliance, articulate the specific strategies you implement to ensure compliance, such as maintaining thorough documentation, access controls, and audit trails. Highlight your familiarity with compliance regulations and emphasize the importance of data governance and security in your past roles, demonstrating your commitment to maintaining high ethical standards in data management.

In your answer, describe a substantial data engineering challenge you faced and the steps you took to solve it. Use the STAR technique (Situation, Task, Action, and Result) to structure your discussion. Explain how you identified the problem, the innovative solutions you implemented, and the impact of those solutions on the team or organization. This demonstrates not only your technical skills but also your critical thinking and problem-solving abilities.

When answering this question, illustrate how you prioritize communication and teamwork. Talk about your approach to regular check-ins, using project management tools, and conducting brainstorming sessions with team members. Emphasize your skill in merging technical insights with business needs, ensuring that all stakeholders are aligned and informed throughout the data engineering lifecycle.

In your response, discuss your time management strategies, such as using prioritization frameworks (e.g., Eisenhower Matrix) or Agile methodologies. Explain how you communicate with stakeholders to understand priorities, adjust timelines, and ensure that project deliverables are met. Highlight your ability to balance urgent tasks with long-term projects without compromising quality.

When discussing your experience with cloud data platforms, detail specific projects where you utilized AWS, GCP, or Azure. Talk about the advantages you found in using these platforms for data storage, retrieval, and scaling solutions. Include any relevant tools or services within these platforms that you have used, such as Lambda for event-driven data processing or BigQuery for analytics.

Focus on specific practices you implement to maintain high data quality, such as regular data validation checks, using automated testing frameworks, and monitoring data pipelines for anomalies. Discuss your experience with error handling and troubleshooting methods to correct data discrepancies, making sure to emphasize the importance of reliable data for business decision-making.

Share experiences of remote work, highlighting challenges such as communication barriers or team integration issues, and explain your solutions, such as establishing clear communication protocols, leveraging collaboration tools, and scheduling regular team meetings. Reflect on how these efforts improved team cohesion and project outcomes, demonstrating your adaptability and proactive mindset in remote work scenarios.

In your answer, define how experimentation influences your approach to data engineering, especially related to A/B testing frameworks. Discuss how you design experiments to validate assumptions and influence data architecture decisions. Provide examples of past takeaways from experimentation that led to meaningful improvements in data processes or system performance.

Reflect on your passion for data engineering and how it aligns with Spotify's vision and products. Mention specific aspects of Spotify's culture that attract you, such as innovation, collaboration, or a focus on music and content delivery. Emphasize how your skills and values align with the company, showing your enthusiasm for the opportunity to contribute to such an exciting platform.