Design, build, and maintain scalable batch and streaming data pipelines using tools like Scio and GCP (BigQuery, Dataflow, GCS)
Develop high-throughput, low-latency pipelines that support use cases such as budget pacing, campaign performance prediction, and auction bidding
Partner closely with machine learning engineers and senior engineers to build data foundations for ML-driven products
Improve data quality, reliability, and observability across pipelines to support critical business decisions
Contribute to architecture and design decisions, balancing long-term scalability with practical delivery
Write clean, maintainable, and well-tested code aligned with Spotify’s engineering practices
Collaborate across functions with product managers, engineers, and stakeholders to deliver impactful solutions

Requirements

experience building and operating distributed data pipelines in production environments at scale
experienced with data technologies such as BigQuery, Bigtable, or similar systems
write production-quality code in Java and/or Scala and have worked with frameworks like Scio or similar
worked with orchestration tools such as Flyte, Styx, or comparable systems
comfortable working with both batch and streaming data processing
approach problems with curiosity and use data to guide decisions and improvements
communicate clearly and collaborate effectively across technical and non-technical teams
interested in working closely with machine learning systems or backend services

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data pipelinesbatch processingstreaming data processingJavaScalaScioBigQueryBigtableFlyteStyx

Soft Skills

communicationcollaborationproblem-solvingcuriositydata-driven decision making