Youll design and build core components of our internal data platform spanning ingestion pipelines semantic layers and metadata systems. Youll help bridge open source technologies (e.g. Kafka Spark Iceberg) with our internal ecosystemshaping how teams discover use and trust data for analytics and AI collaborate with other engineers product managers program managers and end users to understand data needs and evolve platform capabilities that improve scale quality and usability. This is a hands-on engineering role focused on systems thinking technical craftsmanship and delivering tools that unlock real business Responsibilities- Build scalable cloud-native data systems that support data exploration reporting and production ML/AI use cases- Integrate open-source components with internal tools and APIs to streamline platform usability- Develop and maintain data ingestion pipelines metadata services and performance-optimized storage layers- Ensure the platform supports AI-readiness including high-quality discoverable and semantically rich data- Collaborate with internal customers to understand workflows and shape new platform features- Partner with engineers EPMs and US-based teams to ensure alignment reusability and shared standards- Support production systems through monitoring debugging and operational improvements
4 years of experience building distributed data applications or cloud-native platforms
Proficiency in Python Scala or Java with experience developing scalable and maintainable systems
Strong SQL skills and experience with cloud data warehouses (e.g. Snowflake BigQuery)
Experience with modern data infrastructure tools (e.g. Spark Kafka Airflow Iceberg)
Understanding of BI and analytics needs and experience building for internal business use cases
Experience designing and building cloud-based applications APIs and data services
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