Data Engineer AI-driven Data Platform

About The Role

• Build and improve large-scale data platforms that support an AI-powered SaaS product
• Work with tens of terabytes of enterprise data used by real business users
• Design data foundations for future AI / data science use cases
• Work on a proven product with real users and real impact
  
  

• Design, build, and improve big data pipelines for a SaaS + database product
• Develop and maintain data pipelines for:
• Web data crawling
• Data cleansing and enrichment
• Build data quality monitoring and validation systems
• Improve scalability, performance, and cost efficiency of data infrastructure
• Design data platforms and data quality foundations with future analytics and AI usage in mind
  
  

• Python (Scrapy)
• AWS Fargate (Amazon ECS)
• Amazon Managed Workflows for Apache Airflow
  
  

• Amazon S3
• Amazon Aurora PostgreSQL
  
  

• Apache Spark (Databricks)
  
  

• AWS, Databricks
• GitHub, Slack, Notion, Asana, Google Workspace
• Cursor / Claude Code (AI-assisted development, refactoring)
• CodeRabbit (AI-based PR review)
• n8n (AI workflow orchestration)
  
  

• Experience processing data using SQL and Python
• Experience working with public cloud platforms (AWS, GCP, etc.)
• Currently residing in Japan, or willing and able to relocate to Japan
• Business-level Japanese communication skills (equivalent to JLPT N1)
  
  

• Experience with Apache Spark / PySpark
• Experience with AWS analytics services
• Experience using Databricks
• Experience in data management or data quality initiatives
• Interest in or experience with DataOps concepts
  
  

• Employment type: Full-time
• Salary: JPY 4,000,000 – 10,000,000 (depending on experience and skills)
• Fully remote work within Japan
• Full flex-time system (no core hours)
• Stock options available
  
  

• Build and operate data platforms at real production scale
• Work at the intersection of large-scale data and generative AI
• Join a startup with proven PMF entering a major growth phase
• Design data foundations that directly impact product value and decision-making
• Shape data engineering practices and culture from an early stage