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Database Engineer

7M JPY - ***M JPY

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Tokyo

Full-time (permanent)

Online Interview: Yes

Remote Work from Overseas: No / Remote Work in Japan: Yes

Top-Selling Points

Rate of Foreigners

Less Than

Frequency of English Usage

Less Than

Remote work New business Use English Start-up Plans for overseas expansion In-house product development Involved in upstream processes

Related Skills

Experienced area / job

Machine Learning Natural Language Processing

Database

SQL Server

Cloud / Infrastructure / DevOps tools

Azure Google Cloud

Data analysis / Machine learning

BigQuery

Language Requirements

Required

Preferred

Job Description

【Business Overview】
VisasQ operates a knowledge platform connecting over 800,000 experts across 190 countries with a mission of "We make insightful connections possible."

【Department】
・Global ENS (Expert Network Service) Division
Providing high-precision knowledge matching across regional and language barriers for professional clients, including consulting firms and financial institutions. Currently, the development of autonomous AI agents is underway to automate the matching of primary information that cannot be captured by public web information. There is an urgent need to adapt to Japanese language data and resolve differences in data structures between Japan and overseas experts.

【Recruitment Background】
With the widespread adoption of generative AI making the collection of publicly known information easier, the value of individuals' personal experiences and unexpressed information—namely, "primary information"—that cannot be reached on the web has relatively increased. Data spanning multiple products and multiple clouds (Azure/GCP) continues to accumulate daily.

However, the accuracy of AI depends on the quality of the underlying data. Corporate master identity, accuracy of career history data, and reliability of compliance determination—if these wobble, the judgment of AI agents will wobble no matter how much models and prompts are refined.

Currently, step-by-step quality improvements on top of existing structures are underway, but beyond that, the option of rebuilding into a unified master data platform aggregating multiple data sources is also being considered. A professional who can lead this decision-making and design process is required.
Therefore, we are seeking a Data Platform Engineer who can design data platform architectures to create "correct data" on which AI can safely rely, and build up design judgments based on actual measured data without clear-cut answers.

【Job Description】
Leading a series of phases from architectural design of data platforms supporting various applications such as search, analysis, and compliance determination, to data cleansing including entity resolution, and establishing mechanisms to continuously maintain quality.

【Job Details】
・Data Platform Architectural Design
‐Designing architectures to prepare data spanning multiple products and multiple clouds (Azure/GCP) in a form that can be reliably used by AI products, search, analysis, and compliance determination
‐Design judgments based on actual measured data, such as determining where to place the Single Source of Truth (SSOT) and incorporating confidence scores into data models

・Implementation and Operation of Entity Resolution and Data Cleansing
‐Designing and implementing multi-stage entity resolution pipelines combining matching with deterministic keys, scoring, and human review
‐Building data cleansing as a mechanism to continuously maintain quality, including designs ensuring idempotency (where executing the same process multiple times yields the same result) and designs allowing rollback to original states when problems occur
‐Establishing monitoring systems for stable pipeline operation and responding to errors and anomalies

・Data Quality Investigation and Improvement Proposals
‐Investigating data quality issues and identifying root causes
‐Formulating solutions based on business impact

・Technical Decision-Making and Stakeholder Management
‐Documenting designs and investigation findings (design documents, investigation reports, ADRs, etc.)
‐Leading consensus building with CTO, product teams, and global teams

【Development Environment】
・Common ENS Development Environment
- Languages:: C#, Python, Typescript
- Backend Frameworks: ASP.NET Core, FastAPI
- Frontend Frameworks: Angular
- Infrastructure: Microsoft Azure, Google Cloud Platform
- Databases: SQL Server, Cosmos DB
- Communication: Slack, Google meet,Jira
- Documentation: esa, Confluence,
- AI Tools: Claude Code, Devin, ChatGPT

・Data Platform Engineer
- Azure Data Factory
- Microsoft Power BI
- BigQuery

【Highlights of the Role】
・A position where you can feel the rewarding impact of fundamentally elevating the accuracy of AI products
VisasQ is working to rebuild its product into an AI-based system while leveraging existing large assets: a network of 800,000 experts and billions of yen in revenue. However, the barrier to accuracy that cannot be overcome solely through adjustments to models and prompts lies in data quality. You can play a role in fundamentally elevating the quality of the data platform that influences the accuracy of automated research AI agents even more than model refinement.

・Involvement in a seamless scope from issue discovery and design to decision-making with the CTO and implementation
While it is not a situation of building an ideal platform from scratch and presents the difficulty of navigating existing data and mechanisms, precisely for that reason, every single design decision directly connects to the actual product. You can work with high autonomy, being involved end-to-end without functional division—from investigation, measurement, and design to decision-making with the CTO and implementation.

・An environment where data quality improvement is visible through numbers as business impact
Improving the accuracy of corporate masters and career history data moves numbers directly linked to revenue, such as matching rates. Conversely, loose accuracy in compliance determination directly impacts management as unmissable risks. You can work while confirming through daily metrics that the work of data quality improvement directly connects to both business growth and risk management.

・Tackling unique problem settings in a complex environment shaped by acquisition and integration
Following the acquisition of an overseas company (Coleman) in 2021, the Global ENS business has grown while integrating two environments—Japan-originated products and global products—with differing technical platforms and data structures. Precisely due to this history of acquisition and integration, you can tackle large-scale problem settings involving multiple clouds and multiple data sources.

【Impact and Achievements of Global Employees】
Members from various nationalities, including the US, Australia, and South Korea, are actively contributing. You will develop products while collaborating daily with overseas engineering teams in the US and Europe.

【Languages Used】
・English: Used for communication with overseas engineering teams and your assigned team.
・Japanese: Used for communication with members outside your team, such as in company-wide meetings.
*Japanese is not required (feel free to use translation tools; eager learners are welcome!).

【Scope of Work】
・Immediately upon hiring: Please refer to the details above
・Scope of changes: Overall operations within the company

Required Skills and Experiences

【Mandatory Requirements】・Hands-on experience in data engineering as well as design, construction, and operation of large-scale data pipelines and DWH ・Hands-on experience with relational databases (execution plan analysis, index design, batch processing of large-scale data, etc.) ・Hands-on experience in root cause investigation by cross-referencing code and actual data (failure investigation, clarifying data inconsistencies, identifying performance issues, etc.) ・Experience documenting technical designs and investigation findings, and driving consensus based on them (design documents, investigation reports, ADRs, etc.) ・Hands-on backend language development experience ・Business-level Japanese proficiency (native level) 【Preferred Requirements】・English reading, writing, and communication skills (collaboration with global teams is required; translation support systems are in place) ・Experience with the data supply side of AI products, such as LLMs, RAG, and search systems ・Experience in Master Data Management (MDM), entity resolution / record linkage, and data quality management ・Experience leading projects to integrate multiple data sources and build master data platforms (schema integration, ID system design, hitless step-by-step migration using methods like the strangler pattern, reference system cutover planning) ・Development and debugging experience in data synchronization across distributed systems (coexisting event-driven and batch environments) ・Design experience in domains where correctness is strictly required, such as payments, healthcare, authentication, and compliance ・Experience with SQL Server / Azure (AKS, Data Factory) / GCP (BigQuery, Cloud SQL) ・Experience in legacy system investigation and step-by-step migration 【Desired Candidate Profile】・Individuals who enjoy facing actual data and making design decisions without clear-cut answers Those who are not satisfied with superficial operation checks, but pursue data contradictions and discrepancies to reach the root cause, and formulate hypotheses and make decisions based on actual measured data even when no textbook answer exists ・Individuals who can connect technology and business Those who can discuss data quality improvements in business terms, such as matching rates and compliance risks ・Individuals who possess a mindset for maintaining quality through mechanisms Those who do not stop at one-off fixes, but can design sustainable "mechanisms" that continue to function even as data keeps growing ・Individuals who can drive progress while involving decision-makers Those who can document designs and investigation findings, and proactively lead consensus building with team members, including the CTO and global teams

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Working Condition

【Employment Status】
Permanent (Probationary period: 3 months, same terms and conditions apply)

【Working Location】
9F, 4-7-7 Aobadai, Meguro-ku, Tokyo 153-0042, Japan
・At time of hire: Tokyo Head Office and your home
・Scope of change: Locations designated by the company (including Head Office and group companies) and your home

【Estimated Annual Salary】
・Salary: Determined based on skills, experience, and ability (Reviewed twice a year)
・Bonus: Paid once a year (Based on company performance)

【Working System / Workstyle】
・Side jobs: Permitted (Prior approval required)
・Dress code: Casual
・Remote work: Available (From within Japan only | Office attendance required at least once a week)
*The frequency of in-office/remote work is subject to change based on future company policy.

【Working Hours】
Hourly Time Management System OR Discretionary Working System for Professional Work
*The working hours system will be determined based on your skills, experience, assigned duties, and any future updates to internal company regulations.

Hourly Time Management System
Working Hours: 10:00 AM – 7:00 PM

Scheduled Working Hours: 8 hours and 00 minutes / Break: 60 minutes

Discretionary Working System for Professional Work
Deemed Working Hours: 8 hours per day / Break: 1 hour

Average Working Hours: 160 hours / month
(Data from the second half of FY2022, based on the average of development team members)

【Benefits】
・Insurance: Health Insurance, Employees' Pension Insurance, Employment Insurance, Worker's Accident Insurance
・Monthly wellness and productivity allowance (Up to 10,000 yen per month for body care, housework support services, learning, etc.)
・Support for external seminar participation
・Book purchase support
・Annual flu vaccination
・Annual health check-up and optional health screening cost support
・Health insurance benefits (Access to partner resorts, sports facilities, restaurants, etc.)
・Company housing system
・Relocation support for long-distance moves (At time of hire only)
・Financial support for study groups and internal social gatherings
・Internal club activity support
・Lunch allowance for new hire welcome events and internal networking
・Commuter allowance (Up to a specified limit)

【Holidays / Vacations】
Annual holidays: 127 days
・Full two-day weekends (Saturday and Sunday) and Japanese national holidays
・Year-end and New Year holidays
・Paid annual leave (Granted after 3 months of employment)
・Self-development leave (Up to 5 consecutive days per year, separate from annual leave)
・Other legal leaves in accordance with Japanese labor laws

[Measures Against Passive Smoking]
・No smoking indoors, no smoking rooms available / Smoking is strictly prohibited on the entire premises

RECRUITMENT PROCESS

・Document Screening
・Casual Interview
・Interviews (Approx. 3 times)
※The selection process will be conducted primarily online.
※Face-to-face interviews may be requested during the process to assess cultural alignment.
※English interviews with team members from overseas hubs are scheduled for certain stages.
※The number of interviews and the overall process are subject to change.

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