Data Platform Engineer - Scale Data & Personalization (Remote)

Lever, Inc.; Greater London; Hybrid; GBP 65,000 - 90,000; Full time

Job summary

Spotify is seeking a data engineer to join the Data Platform team in London. You will collaborate with product and engineering teams to build scalable data infrastructure and datasets, enabling reliable collection and processing of user behaviour data at scale.

You will work with JVM-based frameworks, SQL and BigQuery, and contribute to personalization and recommendation systems while upholding high-quality code and CI/CD practices.

Qualifications

• Experienced Data Engineer with backend Java development.
• Skilled with large-scale datasets using SQL and BigQuery.
• Experience with JVM data frameworks: Flink, Beam, Dataflow, or Spark.
• Familiar with DevOps, cloud infra, containers and Kubernetes.
• Proficient in data modeling and schema design.
• Commitment to high-quality code, CD and automated testing.
• Appreciates experimentation, iteration, and collaborative work.
• Comfortable with ambiguity and data-driven decision making.

Responsibilities

• Collaborate with product and engineering teams to build data infrastructure.
• Build systems that collect and process user behaviour data at scale.
• Support personalization and recommendation tools with reliable data.
• Design data architectures for high data volumes and broad user base.
• Improve reliability, scalability, and usability of data infrastructure.
• Work across disciplines to solve data challenges and make impact.

Skills

Java

SQL

BigQuery

Flink

Spark

Kubernetes

Cloud platforms

Data modeling

DevOps

Testing/CD

Tools

BigQuery

Spark

Flink

Beam

Dataflow

Kubernetes

Job description

Spotify is seeking a data engineer to join the Data Platform team in London. You will collaborate with product and engineering teams to build scalable data infrastructure and datasets, enabling reliable collection and processing of user behaviour data at scale.

You will work with JVM-based frameworks, SQL and BigQuery, and contribute to personalization and recommendation systems while upholding high-quality code and CI/CD practices.