Data Hub Tech Leader (Digital Bank)

Overview

Under the mission of "Money Forward. Move your life forward," Money Forward aims to resolve the financial concerns and anxieties of individuals and businesses through the power of technology.
We have partnered with Sumitomo Mitsui Financial Group, Inc. and Sumitomo Mitsui Banking Corporation to establish a new company in preparation for the launch of a new digital bank.
We are currently seeking candidates for the position of Data Hub Tech Leader as part of this initiative.
*Based on the press release announced on April 16, 2025.

*This position involves employment with Money Forward, Inc., and a secondment to the new company (SMBC Money Forward Bank Preparatory Corporation). The evaluation system and employee benefits will follow the policies of Money Forward, Inc.

Background of the Recruitment

As a Technical Leader for the MIDAS (Management Integration & Data Analytics System) Data Platform Team, you will lead the design and implementation of the core data hub that connects key systems within one of Japan’s innovative digital banks.

You will architect and drive the development of a modern cloud-based data platform that ingests data from multiple banking systems, applies complex business logic, and serves downstream use cases such as enterprise management, regulatory reporting, and risk management.

Operating in a highly regulated banking environment, you will guide the team through complex data engineering challenges including data quality, cross-system reconciliation, time-critical processing, regulatory compliance, and full data traceability.

This is a technical leadership role where you will define the platform architecture, make critical technology decisions, mentor engineers across levels, and establish best practices that shape the platform’s long-term evolution.

Main Responsibilities

• Lead the design and implementation of the MIDAS data platform architecture, making critical technology selections to build our data hub in a simple, safe and maintainable way despite complex business requirements
• Acquire deep domain knowledge in digital banking data flows, including deposits, loans, payments, AML transaction monitoring, regulatory reporting, and Japanese payment systems
• Define and implement data architecture patterns for Bronze/Silver/Gold layers, ensuring data quality, lineage tracking, and auditability for regulatory compliance
• Lead the team as technical architect, mentoring colleagues about technology usage and best architectural approaches
• Design and implement data governance frameworks including access control, PII protection, audit logging, and retention policies aligned with FISC Security Guidelines
• Establish ingestion and ETL patterns for end-of-day data collection and batch processing workflows
• Design data models and APIs to serve downstream consumers
• Drive technical selection roadmap considering cost optimization, scalability, performance requirements, and cloud migration flexibility
• Conduct technical reviews and establish engineering best practices for data pipeline development
• Set up and monitor cost, security, and performance metrics for the data platform
• Collaborate with Product Owner and Project Manager to translate business requirements into technical solutions
• Work with backend engineering teams to define API contracts for data ingestion from core banking, customer management, and loan origination systems
• Implement data quality reconciliation across multiple source systems
• Lead incident response for data pipeline failures and establish SLAs for data availability
• Champion best practices for data security, privacy, and compliance in a regulated banking environment

Required Skills and Experience

• 8+ years of experience in data engineering, data architecture, or analytics engineering with at least 2 years in technical leadership roles
• Proven track record of designing and implementing large-scale data platforms using Databricks on AWS
• Strong understanding of data modeling techniques: dimensional modeling, data vault, and event-driven architectures
• Hands-on experience with Databricks Workflows
• Experience implementing data governance, security, and compliance controls in regulated industries (financial services, healthcare, or similar)
• Knowledge or hands-on experience in at least one banking domain area: core banking systems, payment systems, regulatory reporting, AML/transaction monitoring, or accounting
• Proven ability to make architecture decisions considering trade-offs between cost, performance, scalability, and maintainability
• Experience leading and mentoring engineering teams (3-10 people), conducting code reviews, and establishing engineering best practices
• Strong programming skills in SQL and Python
• Experience with Infrastructure as Code (Terraform, CloudFormation) and CI/CD pipelines for data platforms
• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience

Preferred Skills and Experience

While not specifically required, tell us if you have any of the following.

• Experience building data platforms in Japanese financial institutions, with knowledge of FISC, FSA, and BOJ requirements
• Knowledge of Japanese payment systems (Zengin, BOJ-NET) and settlement processes
• Knowledge of data quality frameworks and cross-system reconciliation for financial data
• Understanding of banking data models (GL, trial balance, customer 360, product catalogs, etc.)
• Experience designing secure REST APIs with authentication, rate limiting, and SLA management
• Experience with data lineage tracking and data catalog solutions
• Knowledge of data privacy regulations (GDPR, APPI) and data masking/anonymization techniques
• Proven ability to optimize infrastructure costs while maintaining performance
• Experience with DataOps practices, including testing, observability, and incident response
• Ability to clearly explain technical decisions to non-technical stakeholders and executives
• Experience in AI development and/or experience in using AI tools to improve development processes. Money Forward recently announced our AI Strategy roadmap focusing on AI-driven operational efficiencies and integrating AI agents into products.

Language Requirements

• Japanese: Business level
• English: Business level (TOEIC score of 700 or above)

Technology Stack

• Cloud Infrastructure:
  • AWS (primary cloud platform in Tokyo region)
  • S3 for data lake storage with VPC networking for secure connectivity
  • AWS IAM for security and access management
    
    
• Data Lakehouse Architecture:
  • Modern lakehouse architecture using Delta Lake for ACID transactions, time-travel, and schema evolution
  • Columnar storage formats (Parquet) optimized for analytics
  • Bronze/Silver/Gold medallion architecture for progressive data refinement
  • Partition strategies and Z-ordering for query performance
  • Unity Catalog for centralized governance and metadata management
    
    
• Orchestration & Processing:
  • Databricks Workflows for managed workflow orchestration
  • Distributed data processing with Apache Spark on Databricks clusters
  • Serverless compute and auto-scaling clusters for cost optimization
  • Streaming and batch ingestion patterns with Databricks AutoLoader
    
    
• Data Transformation:
  • dbt (data build tool) for SQL-based analytics engineering
  • Delta Live Tables for declarative ETL pipelines with built-in data quality
  • SQL and Python for data transformations
  • Incremental materialization strategies for efficiency
    
    
• Query & Analytics:
  • Databricks SQL for high-performance analytics queries
  • Serverless and auto-scaling SQL warehouses for variable workloads
  • Auto-scaling compute for variable workloads
  • Query result caching and optimization
  • REST APIs for data serving to downstream consumers
    
    
• Data Quality & Governance:
  • Automated data quality with Delta Live Tables expectations and Great Expectations
  • Cross-system reconciliation and validation logic
  • Fine-grained access control with column/row-level security using Unity Catalog
  • Automated data lineage tracking for regulatory compliance
  • Audit logging and 10-year data retention policies
    
    
• Business Intelligence:
  • Amazon QuickSight and/or Databricks SQL Dashboards
  • Integration with enterprise BI tools (Tableau, PowerBI, Looker)

Tools Used

• Version Control: GitHub
• CI/CD: GitHub Actions
• Infrastructure as Code: Terraform
• Monitoring: Databricks monitoring, AWS CloudWatch integration
• AI-Assisted Development: Claude Code, GitHub Copilot, ChatGPT

Development Structure

We operate in a small, agile team while collaborating closely with partners from the banking industry. The MIDAS team is growing rapidly, aiming for more than 10 data engineers within this year.