Senior Data Engineer - MIDAS Data Platform, Digital Bank

必須

• 5+ years of experience in data engineering or analytics engineering
• Strong proficiency in SQL and Python
• Hands-on experience building data pipelines using modern tools (Databricks, Spark, or similar)
• Experience with cloud data platforms (AWS, Azure, GCP) and storage systems (S3, ADLS, GCS)
• Strong understanding of data modeling techniques including dimensional modeling, data vault, or event-driven architectures
• 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
• Language ability: Japanese at Business level required

歓迎

• English proficiency (Business level or above, e.g. TOEIC 700+) is a plus for cross-team documentation and collaboration
• Experience with data quality validation and testing frameworks
• 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 (FI SC Security Guidelines, FSA/BOJ reporting, GDPR, APPI)
• Hands-on experience with Databricks platform (AutoLoader, Unity Catalog, Delta Live Tables, Databricks SQL, Databricks Workflows)
• Experience implementing cross-system reconciliation for financial data
• Experience with performance tuning on Databricks: partitioning strategies, Z-ordering, query optimization, cost management
• Experience building REST APIs with Python (FastAPI, Flask, or similar) for data serving
• Knowledge of streaming data ingestion patterns and external tools (Kafka, Kinesis)
• Experience with Terraform
• Contributions to open-source data engineering projects
• Experience with Databricks SQL Dashboards or BI tools (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.

使用ツール

Overview

• As a Senior Data Engineer in the MIDAS (Management Integration & Data Analytics System) Data Platform Team, you will build from scratch and maintain the central data hub connecting most systems found inside one of Japan's more innovative digital banks.
• You will work with modern cloud-based data technologies to ingest data from various banking systems, apply complex business logic on it, then serve it to downstream systems for enterprise management, regulatory reporting, risk management and many other applications.
• Thanks to the high expectations towards the banking domain, you will have the opportunity to work on complex data engineering challenges including data quality, reconciliation across multiple systems, time-critical data processing, and complete traceability.
• This is a senior individual contributor role where you will design and implement complex data pipelines, mentor mid-level engineers, and participate in architectural decisions for the platform.
• ※ 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.

Responsibilities and Duties

• Design and implement data pipelines to ingest data from multiple source systems (CRM, CBS, CLM, LOS) using REST APIs or database connections
• Build and maintain Bronze/Silver/Gold layer transformations ensuring data quality, consistency, and performance
• Implement data quality checks and cross-system reconciliation logic (e.g., validating CBS transaction records against ledger balances)
• Develop and optimize SQL queries and transformations using Databricks SQL/notebooks or Delta Live Tables
• Design and implement data models for analytics and reporting use cases, with a primary focus on CRM data integration, alongside regulatory reporting and the outbound Conseek risk-export mart (ALM/ERM risk computation itself is handled externally by Conseek)
• 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
• Support EOD (End-of-Day) data collection processes that align with Zengin settlement timing

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 (Databricks)

• 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)

• 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 (Databricks)

• Delta Live Tables for declarative ETL pipelines with built-in data quality
• SQL and Python for data transformations in Databricks notebooks
• Incremental materialization strategies for efficiency

Query & Analytics (Databricks)

• Databricks SQL for high-performance analytics queries
• Serverless and auto-scaling SQL warehouses for variable workloads
• Query result caching and optimization
• REST APIs for data serving to downstream consumers
• Direct Delta Lake access for advanced consumers (Athena, Redshift Spectrum, etc.)

Data Quality & Governance (Databricks)

• Automated data quality with Delta Live Tables 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 (Databricks)

• Databricks SQL Dashboards for internal analytics and monitoring
• Integration with enterprise BI tools (Tableau, PowerBI, Looker) via Databricks SQL endpoints

Development & DevOps

• Languages: SQL (primary), Python
• Platform: Databricks (notebooks, workflows, SQL)
• 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