Lead, mentor, and grow a team of Data Ingestion Engineers; foster psychological safety, operational excellence, ownership, and continuous improvement.
Define and drive the long-term technical vision and roadmap for Mercari’s data ingestion platform; lead architectural discussions for large-scale distributed data systems; ensure scalability, reliability, and developer productivity.
Oversee reliability and operational health of ingestion pipelines and event processing; manage incidents and post-incident reviews; enhance observability, SLIs/SLOs, monitoring, and cost-aware operations.
Collaborate with Data Management, BI Analytics, SRE, Platform Engineering, and product teams to deliver reliable, scalable data delivery for analytics, experimentation, and ML; expand data sources to 3rd party, unstructured, HR/Finance; promote self-service ownership.
Hire, onboard, and develop engineers; manage execution planning, prioritization, and roadmap delivery.
技術スタック
必須スキル
Experience designing, developing, and operating large-scale distributed data pipelines or services; proficiency in Go, Python, Java, or Scala.
Strong ability to write design documents/technical proposals and align stakeholders; excellent cross-functional communication.
歓迎スキル（該当する場合）
Streaming data processing frameworks: Apache Beam, Spark, or Flink.
Data Warehouses: Google BigQuery, Amazon Redshift, Hive/Hadoop, Snowflake.
Monitoring/alerting tools; Google Cloud Platform (Dataflow, Pub/Sub, GKE, Compute Engine).
Confluent Cloud or Apache Kafka; workflow engines like Argo Workflow or Apache Airflow.
OSS contributions.
キャリア成長観点
Opportunity to shape the long-term architecture and roadmap of a high-volume data ingestion platform, directly impacting analytics and ML capabilities across Mercari.
Develop leadership, hiring, and cross-functional collaboration skills by guiding engineers and aligning multiple teams (Data Management, BI Analytics, SRE, Platform Engineering) with business goals and data strategy.
Gain experience in cost optimization (FinOps) and platform reliability at scale, while expanding data coverage to new sources (3rd party, unstructured, HR/Finance) and driving Data × AI initiatives.