[XC-AS] DataOps Engineer - ADAS Data Platform
HybridYokohama, Kanagawa, Japan
Job Summary
Own platform operations for cloud-based data ingestion, recording data management, metadata management, data quality validation, preprocessing, harmonization, dataset production, and ML/recompute workflows across the data lifecycle. Monitor and improve the health of data pipelines and platform services to ensure data freshness, completeness, correctness, and processing performance. Build and maintain dashboards, monitoring, alerting systems, runbooks, and escalation paths to drive operational excellence through automation and process optimization. Ensure datasets and pipelines remain traceable, recoverable, and reliably supported for global customer projects.
Required Qualifications
- Business-level English communication skills.
- Experience operating data platforms, data pipelines, cloud platforms, or distributed systems in production.
- Experience with monitoring, alerting, incident response, troubleshooting, root-cause analysis, and operational documentation.
- Experience with Microsoft Azure or a comparable cloud platform.
- Understanding of data ingestion, data processing, ETL/ELT concepts, data quality validation, metadata management, and data lifecycle management.
- Experience with scripting or automation using Python, PowerShell, Bash, or similar tools.
- Working knowledge of SQL for data investigation, validation, troubleshooting, and log analysis.
- Familiarity with Linux, Git, and DevOps/DataOps ways of working.
- Strong communication skills and the ability to collaborate effectively with technical and non-technical stakeholders across regions.
Desired Qualifications
- Business-level Japanese communication skills.
- Experience with Azure services such as Azure Storage, Databricks, Azure Monitor, or related cloud-native data platform technologies.
- Experience in ADAS, automated driving, vehicle measurement systems, sensor data, or automotive data platforms.
- Experience with Spark, Databricks, Argo Workflows, Airflow, Docker, Kubernetes, Terraform, Bicep, or GitHub.
- Experience supporting machine learning workflows, dataset production, labeling workflows, model training, recompute, or validation workflows.
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