Appealing Points
- An early-career role offering structured mentoring and hands-on experience across the full data lifecycle
- Opportunity to work with modern Azure cloud data services, lakehouse architecture, DataOps practices, and approved AI coding assistants
- Collaborate in an agile, multinational team alongside data engineers, platform/SRE engineers, business analysts, and governance stakeholders
Annual Salary: ¥5 million and above
Job Description
We are seeking a Cloud Data Engineer to join the Data Platform Team and help build trusted, secure, and reusable data products for an insurance business. Working under the guidance of senior engineers, the successful candidate will develop and operate cloud-based data pipelines that connect internal and external sources with analytics, reporting, operational, and AI use cases. This is an early-career role for candidates with a strong foundation in SQL and programming, and a willingness to learn Microsoft Azure data services, modern lakehouse and ETL/ELT practices, DataOps, and insurance data controls. The role contributes through hands-on coding, testing, documentation, monitoring, and problem solving rather than team leadership or vendor management, and offers structured mentoring and opportunities to grow into a well-rounded cloud data engineer.
Job Responsibilities
- Develop, test, deploy, and support batch or near-real-time ETL/ELT pipelines using approved Azure services and engineering standards
- Write clear, maintainable SQL and Python or PySpark code for data ingestion, transformation, validation, and delivery
- Assist with source-to-target mapping, data modeling, metadata, cataloging, lineage, and technical documentation
- Implement automated unit, integration, reconciliation, and data-quality checks, including handling schema changes, duplicates, late data, and failed records
- Use Git and CI/CD practices to manage code, peer reviews, releases, and environment promotion in an auditable manner
- Monitor pipeline health, data freshness, job duration, and failures; follow runbooks, investigate issues, and escalate promptly when needed
- Participate in incident, problem, change, and release-management activities, including evidence collection and post-incident improvement actions
- Apply least-privilege access, secure coding, secrets management, privacy, retention, and regulatory controls for sensitive insurance data
- Collaborate with business analysts, source-system owners, data consumers, and global or offshore team members to clarify requirements and deliver incremental value
- Use approved AI coding assistants responsibly to improve productivity, while validating outputs and protecting confidential data
Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience
- Approximately 0–2 years of relevant experience through employment, internship, academic projects, bootcamp, or a personal portfolio; recent graduates are welcome
- Working knowledge of SQL, including joins, aggregations, common table expressions, and basic query troubleshooting
- Basic programming ability in Python or another general-purpose language, with an understanding of functions, data structures, error handling, logging, and testing
- Understanding of relational databases, files such as CSV, JSON, or Parquet, data types, keys, normalization, and basic dimensional-modeling concepts
- Basic familiarity with cloud concepts and at least one data service or hands-on learning environment in Azure, AWS, or GCP; willingness to deepen Azure skills
- Familiarity with Git, pull requests, and the fundamentals of CI/CD, automated testing, and agile delivery
- Awareness of data quality, security, privacy, access control, and responsible handling of customer information
- Strong learning mindset, attention to detail, logical problem-solving, and the ability to ask for help and communicate progress clearly
- Ability to collaborate in a multinational environment using business-level Japanese and practical English for technical communication, or a clear commitment to develop the weaker language
Preferred Qualifications
- Exposure to Azure Data Factory or Synapse Pipelines, Azure Data Lake Storage, Azure SQL, Microsoft Fabric, Databricks, or Apache Spark
- Experience creating a small end-to-end data pipeline, data model, dashboard dataset, API integration, or automated data-quality check
- Familiarity with orchestration, monitoring and observability, Infrastructure as Code, containers, or Linux command-line tools
- Interest in insurance or financial services, including policy, customer, claims, distribution, finance, or regulatory data
- Relevant entry-level certification, such as Microsoft Azure Fundamentals or Azure Data Fundamentals, or equivalent learning evidence
- Preferable: domain knowledge of Life Insurance
Language Proficiency: Business-level Japanese (N2 or equivalent) and Business Level English
Company Description:
We are a technology-led software services company that fulfills technology companies' software product development needs worldwide. As a trusted partner of today’s enterprises, we build software solutions for the future. We aim to understand our customers' needs and aspirations to deliver software that aligns with their vision. We are passionate about creating future-ready software focusing on superior user experience and ultimate simplicity.Specializing in mobile, web, and cloud-based software development services across various industry verticals, we cater to customers globally. We focus on developing next-generation software products that require a deep understanding of communication networks, operating system internals, device diversity, and critical system resources. Our core strength lies in combining these engineering environments to create cutting-edge software solutions.
. Skillset Required: SQL, Python, PySpark, Data ingestion, Data transformation, Data validation, Data delivery, Source-to-target mapping, Data modeling, Metadata management, Cataloging, Data lineage, Technical documentation, Automated unit testing, Integration testing, Reconciliation checks, Data-quality checks, Schema change handling, Duplicate data handling, Late data handling, Failed records handling, Git, CI/CD, Code peer reviews, Code releases, Environment promotion, Pipeline monitoring, Incident management, Problem management, Change management, Release management, Evidence collection, Post-incident improvement, Least-privilege access, Secure coding, Secrets management, Privacy controls, Data retention, Regulatory controls, AI coding assistant usage, Agile collaboration, Cross-functional communication, Attention to detail, Logical problem-solving, Business-level Japanese, Business-level English, Functions (programming), Data structures, Error handling, Logging, Testing, Relational databases, CSV files, JSON files, Parquet files, Data types, Keys, Normalization, Dimensional modeling, Cloud concepts, Azure Data Factory, Azure Synapse Pipelines, Azure Data Lake Storage, Azure SQL, Microsoft Fabric, Databricks, Apache Spark, Data pipeline development, Dashboard dataset creation, API integration, Orchestration, Monitoring and observability, Infrastructure as Code, Containers, Linux command-line tools
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