Engineering Manager, Data Ingestion - Mercari

業務内容

• 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.