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 Senior Data Engineer as part of this initiative.
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.
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.
Responsibilities
- Design and implement data pipelines to ingest data from multiple source systems using Databricks native tools, as well as REST APIs
- Build and maintain Bronze/Silver/Gold layer transformations on Databricks ensuring data quality, consistency, and performance.
- Implement data quality checks and cross-system reconciliation logic.
- Develop and optimize SQL queries and transformations using dbt or similar tools.
- Design and implement data models for analytics and reporting use cases (ALM, ERM, regulatory reporting).
- Build REST APIs or data serving layers for downstream consumers.
- Participate in architecture decisions for data platform components.
- Write unit tests, integration tests, and data quality tests for pipelines.
- Monitor data pipeline performance, troubleshoot failures, and implement improvements.
- Optimize query performance through partitioning strategies, Z-ordering, and query tuning.
- Implement infrastructure as code for data platform components using Terraform.
- Set up CI/CD pipelines for automated testing and deployment of data pipelines.
- Mentor mid-level engineers and conduct code reviews.
- Contribute to documentation and best practices for the team.
- Collaborate with backend engineers to define API contracts and data schemas.
- Work with Technical Lead on platform design and technology selection decisions.
- Lead features and initiatives within the data platform.
Requirements
- 5+ years of experience in data engineering with data focus or analytics engineering.
- Strong proficiency in SQL and Python.
- Hands-on experience building data pipelines using modern tools (Databricks, Spark, dbt, or similar).
- Experience with databricks development and with AWS cloud environments
- Strong understanding of data modeling techniques including 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.
- Experience with version control (Git) and CI/CD pipelines.
- Understanding of data governance concepts: access control, audit logging, data lineage.
- Strong problem-solving skills and ability to work independently.
- Experience mentoring junior or mid-level engineers.
- Excellent communication skills for collaborating with cross-functional teams.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Japanese: Business Level (Fluent, capable of handling communication with clients in Japanese)
Nice to haves
While not specifically required, tell us if you have any of the following.
- Experience in financial services, fintech, or other regulated industries.
- Knowledge of banking domain concepts: core banking systems, payment processing, regulatory reporting, AML/transaction monitoring.
- Experience implementing data platforms that comply with regulatory requirements (FISC Security Guidelines, FSA/BOJ reporting, GDPR, APPI).
- Experience implementing cross-system reconciliation for financial data.
- Experience with performance tuning: partitioning strategies, query optimization, cost management.
- Experience building REST APIs with Python (FastAPI, Flask, or similar) for data serving.
- Knowledge of streaming data pipelines (Kafka, Kinesis, or similar).
- Experience with Terraform.
- Contributions to open-source data engineering projects.
- Experience with BI tools (QuickSight, Tableau, Looker, PowerBI).
- Experience leading technical initiatives from design through implementation.
- Track record of improving data platform performance or reducing costs (provide specific metrics).
- Experience in AI development and/or experience in using AI tools to improve development processes.
- Money Forward recently announced our AI Strategy roadmap which focuses on improving AI-driven operational efficiencies, as well as integrating AI agents into our products to deliver better value to our users.
Compensation
¥5,808,000 ~ ¥11,004,000 annually.
