業務内容

• Design, build, maintain, and optimize large-scale cloud-native data ingestion, storage, processing, and serving systems.
• Align data ecosystem with ML development needs and tailor pipelines for ML workloads.
• Collaborate with Data Infrastructure, Site Reliability Engineering, ML Platform, CV/ML teams on high-impact AD/ADAS projects.
• Mentor junior engineers and promote engineering best practices.
• Contribute to the long-term strategy for data ingestion and processing platforms.

技術スタック

必須スキル

• 5+ years of professional software engineering with strong data structures/algorithms; proficient in Python, Go, or Java.
• 3+ years designing/building data-intensive, concurrent, scalable applications.
• 3+ years experience with data platforms, data pipelines, orchestration, batch processing, and/or distributed databases.
• Cloud-based (AWS/GCP) microservice architectures; event-driven, distributed systems.
• Bachelor's degree in CS or related field (or equivalent); business-level English and Japanese.

歓迎スキル（該当する場合）

• 2+ years writing testable, modular code in Python/Go/Java.
• Site Reliability Engineer experience; Terraform, Docker, Kubernetes in production.
• Multi-region/multi-cloud deployments; data governance, privacy and security.
• Ability to thrive in fast-paced, cross-team environments.

キャリア成長観点

• 大規模データ ingestion（数百TB/day）と Lakehouse 基盤に携わり、AD/ADASとML開発を支えるデータエコシステムを形作る成長機会。
• クロスファンクショナルな協働（Data Infra/SRE/ML Platform/CV/ML）を通じた幅広い技術領域の習得。
• ジュニアの指導・ベストプラクティスの醸成、長期戦略への寄与を通じたリーダーシップ育成。
• マルチリージョン/マルチクラウド、クラウドネイティブ技術、データガバナンス等の実務経験を積み、キャリアパスとしてSenior Data Engineer、Platform Engineer、SRE、Cloud Architect等へ拡張可能。