Data Platform Engineer - OLAP

• Performance at Scale: Develop and maintain high-performance OLAP systems, supporting multi-tenant query workloads and ingestion pipelines with real Big Data scale
• Reliability: Work with system reliability in mind, ensuring high availability for business-critical analytical products through observability and engineering excellence
• Productize the Platform: Build self-service tooling that enables Data Engineers and Analysts to independently manage their data assets and diagnose issues
• Data Quality: Engineer automated frameworks to validate data integrity and leverage metadata-driven tools to enhance data discoverability, lineage, and cataloging across the ecosystem
• Ecosystem Integration: Architect seamless integrations of the OLAP ecosystem with adjacent distributed systems (e.g. storage, messaging, and batch / stream processing systems)
• Efficiency & Governance: Monitor and optimize cluster resource-efficiency while making sure the platform adheres to global security and data privacy standards

• Fluency in Python and/or Java
• Team player with strong communication skills
• Ability to work closely with diverse stakeholders you enable (analysts, data scientists, data engineers, etc.) and depend upon (infrastructure, security, etc)
• Experience in OLAP technologies, like Druid, Clickhouse, Pinot, Doris, Starrocks, etc
• Experience in CI/CD pipelines, for code and infrastructure automation
• Experience in Kubernetes
• Experience in infrastructure and large-scale private cloud systems
• Additional experience developing and maintaining: Other distributed data and compute systems like Spark, Trino, etc
• Data modelling for databases
• Real-time and batch data pipelines (via Kafka, Spark streaming) with an eye for frameworks, and emphasis on user friendliness and quality

Golang or Rust are also appreciated