Data
We are looking for a Data Engineer to help us build structured, high-quality data solutions for our Content Understanding teams. Our teams process petabytes of data using tools such as BigQuery and Dataflow, and where needed we develop our own data tooling to support and evolve our music catalog.
Location
London
Job type
Permanent

What You’ll Do

• Take ownership over, maintain and improve data pipelines that are integral to our success
• Evaluate and optimize our offerings for efficiency and reliability at scale
• Identify areas of architectural improvement to remove the potential for human error
• Collaborate with product and engineering teams to understand data requirements and create innovative solutions to challenging problems
• Understand the analytics data we collect from our systems and how it relates to the business value being produced

Who You Are

• Proven experience in data engineering, including creating reliable, efficient and scalable data pipelines for large data sets (especially in Python or Scala)
• Experience in building and maintaining scalable backend systems
• Knowledge of cloud-based data platforms (AWS, GCP or Azure)
• Strong collaboration and communication skills with technical and non-technical stakeholders
• Seek to participate in continuous improvement efforts such as software development, planning, estimating, developing and testing

Where You’ll Be

• This role is based in London (UK)
• 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

Our global benefits
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 - fully paid - 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.
Learn about life at Spotify

• Take ownership over, maintain and improve data pipelines that are integral to our success
• Evaluate and optimize our offerings for efficiency and reliability at scale
• Identify areas of architectural improvement to remove the potential for human error
• Collaborate with product and engineering teams to understand data requirements and create innovative solutions to challenging problems
• Understand the analytics data we collect from our systems and how it relates to the business value being produce