業務内容

• Define the ML platform strategy and roadmap; translate business needs into technical specifications and architecture.
• Design and oversee large-scale data engineering (ETL/DWH/data lakes) for high-traffic environments using Databricks and AWS; optimize performance.
• Establish governance and security by design (IAM, permissions, auditable foundations) aligned with model risk management principles.
• Align with Engineering, Legal, Compliance, and Business to prioritize initiatives and drive cross-functional execution.
• Own end-to-end delivery from technology selection to operations; drive platform standardization across the company.

技術スタック

必須スキル

• Software engineering ≈5+ years (backend/infrastructure/data platforms) and technical leadership experience.
• Architecture decisions/technical direction as tech lead or PM.
• Data engineering experience with large-scale datasets (ETL/DWH/data lakes) and performance tuning.
• Strong documentation skills for clear specs and design rationale.

歓迎スキル（該当する場合）

• Hands-on MLOps with SageMaker and/or Databricks.
• Financial domain knowledge (credit/risk) and familiarity with FISC security guidelines.
• Product management track record and roadmap ownership under technical constraints。

キャリア成長観点

-Enterprise-scale ML Platform in finance: shape architecture, governance, and security end-to-end.

-Cross-functional impact via collaboration with Legal/Compliance and Business; influence company-wide MLOps standards.

-Hands-on exposure to AWS/SageMaker/Databricks and data governance; tangible platform-level impact.

-Develop leadership, strategic planning, and delivery discipline alongside technical excellence.