Data Engineer AI Platform

Responsibilities: - Design, build, and maintain scalable cost and usage data pipelines using SQL and Python in cloud data environments (e.g., Snowflake, Databricks). - Own ingestion and transformation logic for platform-wide usage and billing data. - Develop and enhance cost allocation models (compute, storage, and shared overhead), ensuring alignment with cloud billing. - Deliver cost visibility through well-designed data models, warehouse views, and API endpoints for internal stakeholders. - Establish data quality, observability, and reliability standards (SLIs/SLOs) for pipeline freshness and accuracy. - Collaborate cross-functionally with Cloud Engineering, FinOps, and Business Operations teams to translate requirements into scalable data solutions. - Support broader data engineering initiatives, including additional ingestion and warehousing use cases. Profile Requirements: - 5+ years of experience in data engineering or backend development using SQL and Python. - Strong experience with cloud billing datasets (e.g., AWS, GCP) and understanding of pricing models. - Proven ability to design and operate data models for financial reporting or cost allocation in modern data warehouses. - Hands-on experience with Snowflake, Databricks, or similar platforms. - Understanding of FinOps principles such as chargeback, showback, tagging strategies, and cost optimization. - Experience working cross-functionally with engineering, product, and business stakeholders. - Business-level English communication skills. Nice to Have: - Experience with BI tools (e.g., Power BI) and financial dashboards. - Familiarity with FinOps tooling (e.g., Kubecost). - Exposure to AI/ML platforms, MLOps, or training/inference infrastructure. - Experience in multi-cloud or multi-tenant environments. - Business-level Japanese.