Product Manager owning Spotify’s data catalogue, metadata, and lineage roadmap. Improving how engineers, analysts, and AI agents discover and safely use data.
About the role
- Senior Data Platform Engineer building scalable data platforms for Mistplay, a mobile gaming loyalty app. Owning ingestion, lakehouse, pipelines, observability, and low-latency data access.
Responsibilities
- Report to the Director of Data Platform
- Build and operate core systems enabling reliable, scalable, high-velocity data access and analytics across Mistplay
- Own significant data platform components and contribute to technical direction
- Build and maintain scalable ingestion systems for batch and streaming data sources
- Implement schema evolution, data contracts, and end-to-end lineage
- Contribute to compute, performance, and cost optimization
- Implement and evolve data warehouse, lakehouse, or hybrid analytical platforms
- Build and maintain transformation pipelines using tools such as dbt and Spark
- Implement orchestration, dependency management, data quality contracts, and testing frameworks
- Implement low-latency data access systems using caching, materialization, and API strategies
- Contribute to SLAs for data freshness, consistency, and query performance
- Implement data quality monitoring, anomaly detection, and freshness checks
- Participate in incident response and postmortems for data reliability
- Improve data discoverability, ownership, documentation, and self-service access
- Evaluate and integrate platform components such as Spark, dbt, Airflow, Kafka, and data catalogs
- Contribute to migrations and platform improvements with minimal downstream disruption
- Partner with Data Science, ML Platform, and Backend to reduce data latency, increase analytical throughput, and improve data trust
Requirements
- 7–8+ years of experience building and operating production data platforms
- Proven ownership of components within large-scale systems supporting real-time or near-real-time data access and analytical workloads
- Strong proficiency in Python, Scala, or Go
- Experience building and evolving distributed data systems with high reliability, maintainability, and rigorous engineering standards
- Solid expertise in modern data warehouse and lakehouse architectures, including Snowflake, BigQuery, Databricks, Delta Lake, or Iceberg
- Strong experience designing and operating batch and streaming data pipelines
- Understanding of streaming systems such as Kafka and Flink, and batch frameworks such as Spark and dbt
- Experience with data modeling, transformation frameworks, and testing practices
- Operational rigor with metrics, logs, data quality alerts, SLOs, cost optimization, and incident response
- Participation in design reviews and architectural discussions
- Experience mentoring teammates and owning complex platform components
- Ability to collaborate across Data Science, ML Platform, Analytics, DevOps, and Backend
- Ability to communicate technical trade-offs and translate requirements into executable platform work
Benefits
- Team lunches
- Game nights
- Company-wide events
- Virtual and in-person perks and events
- Growth-oriented work culture
- Opportunities to share ideas, push boundaries, take calculated risks, and bring visions to life
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Degree requirement
No Education Requirement
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