Senior Data Engineer — Digital Bank (Tokyo)

Role conditions

Japan residency required — this role does not include relocation or visa sponsorship. Applications from outside Japan cannot be considered.

Business-level Japanese required.

Context

Our client is building a new digital bank in Tokyo, born from a joint venture between a major Japanese fintech player and a leading banking group. The mission: build embedded financial products from the ground up, with a strong focus on supporting small and medium-sized businesses (SMBs).

About the role

As a Senior Data Engineer on the Data Platform team, you will build and maintain the central data hub connecting most systems inside this new digital bank. You will ingest data from banking systems, apply business logic, then serve it to downstream systems for enterprise management, regulatory reporting and risk management. This is a senior individual contributor role: you will design and implement complex data pipelines, mentor mid-level engineers, and participate in architecture decisions for the platform.

Team

Small agile team of 11 engineers, working in close collaboration with banking-side stakeholders.

Technology stack

• Cloud — AWS (Tokyo region), S3 data lake, VPC networking, IAM
• Databricks lakehouse — Delta Lake (ACID, time-travel, schema evolution), Bronze/Silver/Gold medallion architecture, Unity Catalog
• Orchestration & processing — Databricks Workflows, Apache Spark, batch and streaming ingestion with AutoLoader
• Transformation — Delta Live Tables, SQL and Python, incremental materialization
• Analytics — Databricks SQL, auto-scaling SQL warehouses, REST APIs for data serving
• Dev & DevOps — SQL, Python, GitHub, GitHub Actions, Terraform, AI-assisted tooling (Claude Code, GitHub Copilot)

Responsibilities

1. Design and implement data pipelines ingesting from multiple source systems via REST APIs or database connections
2. Build and maintain Bronze/Silver/Gold transformations ensuring data quality, consistency and performance
3. Implement data quality checks and cross-system reconciliation logic
4. Develop and optimize SQL queries and transformations using Databricks SQL/notebooks or Delta Live Tables
5. Design data models for analytics and reporting, with a primary focus on CRM data integration alongside regulatory reporting and downstream risk-management exports
6. Build REST APIs or data serving layers for downstream consumers
7. Participate in architecture decisions for data platform components
8. Write unit, integration and data quality tests for pipelines
9. Monitor pipeline performance, troubleshoot failures, implement improvements
10. Optimize query performance (partitioning, Z-ordering, query tuning)
11. Implement infrastructure as code using Terraform
12. Set up CI/CD for automated testing and deployment
13. Mentor mid-level engineers, conduct code reviews

Profile

• Currently based in Japan with valid work authorization — no relocation or visa sponsorship from overseas for this role
• Business-level Japanese required
• 5+ years in data engineering or analytics engineering
• Strong proficiency in SQL and Python
• Hands-on experience building data pipelines with modern tools (Databricks, Spark, or similar)
• Experience with cloud data platforms (AWS, Azure, GCP) and storage (S3, ADLS, GCS)
• Strong data modeling: dimensional modeling, data vault, or event-driven architectures
• Experience with data quality validation and testing frameworks
• Proven ability to debug and optimize slow queries and data processing jobs
• Version control (Git) and CI/CD pipelines
• Data governance concepts: access control, audit logging, data lineage
• Strong problem-solving, ability to work independently
• Experience mentoring junior or mid-level engineers
• Bachelor's degree in CS, Engineering or related, or equivalent practical experience

Nice to have

English business level (TOEIC 700+) · financial services / fintech / regulated industries · banking domain (core banking, payments, regulatory reporting, AML) · regulatory-compliant data platforms (FISC, FSA/BOJ, GDPR, APPI) · Databricks hands-on (AutoLoader, Unity Catalog, DLT, Databricks SQL, Workflows) · cross-system reconciliation for financial data · Databricks performance tuning and cost management · REST APIs with Python (FastAPI, Flask) · streaming ingestion (Kafka, Kinesis) · open-source contributions.