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포지션 상세
[필독: 지원 자격 안내]
• 어학 자격: 비즈니스 레벨 이상의 영어, 일본어 능력 필수 (모든 업무는 영어로 진행)
• 제출 서류: 영문 레쥬메(Resume/CV) 제출 필수
주의: 국문/일문 이력서만 제출 시 또는 영어 소통 불가 시, 별도 통보 없이 서류 전형에서 제외됩니다.
이 포지션은 LAPRAS Global Bridge 채용건이며, 근무지는 일본입니다.
＃Recruitment Background
VISASQ operates a knowledge platform dedicated to its mission: "We make insightful connections possible." Connecting businesses with precisely the right expertise across 190+ countries and a network of over 800,000 experts, our Global Expert Network Service (ENS) provides high-precision matching across region and language barriers for professional clients, including management consulting firms and financial institutions.
In an era where generative AI has made publicly available web information easily accessible, the value of unstructured first-hand human experience and knowledge has grown exponentially. Currently, vast amounts of data continue to accumulate across multiple products and multi-cloud environments (Azure and GCP).
However, the precision of AI agents relies fundamentally on the quality of the underlying data. Core elements—such as company master identity resolution, employment history accuracy, and compliance verification reliability—directly impact the quality of AI agent decision-making.
While we are incrementally improving data quality within our current structures, we are looking toward building a unified master data platform that consolidates multiple data sources. We are seeking a Data Platform Engineer who can design the data platform architecture that fuels our AI products and lead complex, data-driven design decisions based on empirical evaluation.
＃Highlights of the Role
■Fundamentally Elevate AI Product Precision:
Drive the foundational quality of the data architecture that directly controls the accuracy limits of autonomous AI research agents and matching engines.
■End-to-End Ownership from Investigation to CTO Decision-Making:
Work directly with complex, real-world legacy and multi-cloud data setups. You will own the full lifecycle—from data investigation and architecture design to CTO alignment and implementation.
■Direct Business Impact:
Quality improvements in company entity resolution and career history directly drive core matching rates (revenue) while minimizing critical compliance risks.
■Navigate Complex Post-Acquisition Environments:
Gain rare experience solving large-scale data engineering problems arising from combining distinct tech stacks, data models, and cross-border operations following our 2021 global acquisition of Coleman.
Detailed Scope:
■Data Platform Architectural Design:
• Design data models across multiple products and multi-cloud environments (Azure/GCP) to ensure reliable data supply for AI products, search engines, analytics, and compliance checks.
• Define Single Source of Truth (SSOT) strategies and integrate confidence scoring into data models based on empirical findings.
■Entity Resolution & Data Cleansing Pipeline:
• Design and implement multi-stage entity resolution pipelines combining deterministic matching, scoring models, and human-in-the-loop review workflows.
• Build robust data cleansing processes that enforce idempotency and state rollback capabilities to maintain high data integrity over time.
• Establish monitoring and anomaly detection mechanisms to ensure stable pipeline operations.
■Data Quality Research & Problem Solving:
• Investigate data quality issues, identify root causes within data/codebases, and formulate impactful technical solutions based on business metrics.
■Technical Decision-Making & Stakeholder Alignment:
• Document architectural designs, research findings, and Architectural Decision Records (ADRs).
• Drive consensus with the CTO, local product teams, and global engineering leaders.
＃Technical Environment
[ENS Development Common Stack]
Programming Languages: C#, Python, TypeScript
Backend Frameworks: ASP.NET Core, FastAPI
Frontend Framework: 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 Specific Tools]
Azure Data Factory
Microsoft Power BI
BigQuery
• Relational Database Expertise: Deep hands-on experience with SQL execution plan analysis, index optimization, and high-volume batch processing.
• Root-Cause Data Investigation: Experience cross-analyzing code logic and raw data to resolve data inconsistencies, pipeline outages, and performance bottlenecks.
• Technical Documentation & Consensus Building: Strong ability to document architectural choices (design docs, research reports, ADRs) and align technical direction with stakeholders.
• Backend Development: Hands-on experience developing backend applications in modern programming languages.
• Language Proficiency: Native or bilingual level Japanese proficiency (essential for analyzing complex local data schemas and collaborating with Japanese domain teams).
・Fluent Level Japanese
・Business Level English
・Relocation to Japan required
• 어학 자격: 비즈니스 레벨 이상의 영어, 일본어 능력 필수 (모든 업무는 영어로 진행)
• 제출 서류: 영문 레쥬메(Resume/CV) 제출 필수
주의: 국문/일문 이력서만 제출 시 또는 영어 소통 불가 시, 별도 통보 없이 서류 전형에서 제외됩니다.
이 포지션은 LAPRAS Global Bridge 채용건이며, 근무지는 일본입니다.
＃Recruitment Background
VISASQ operates a knowledge platform dedicated to its mission: "We make insightful connections possible." Connecting businesses with precisely the right expertise across 190+ countries and a network of over 800,000 experts, our Global Expert Network Service (ENS) provides high-precision matching across region and language barriers for professional clients, including management consulting firms and financial institutions.
In an era where generative AI has made publicly available web information easily accessible, the value of unstructured first-hand human experience and knowledge has grown exponentially. Currently, vast amounts of data continue to accumulate across multiple products and multi-cloud environments (Azure and GCP).
However, the precision of AI agents relies fundamentally on the quality of the underlying data. Core elements—such as company master identity resolution, employment history accuracy, and compliance verification reliability—directly impact the quality of AI agent decision-making.
While we are incrementally improving data quality within our current structures, we are looking toward building a unified master data platform that consolidates multiple data sources. We are seeking a Data Platform Engineer who can design the data platform architecture that fuels our AI products and lead complex, data-driven design decisions based on empirical evaluation.
＃Highlights of the Role
■Fundamentally Elevate AI Product Precision:
Drive the foundational quality of the data architecture that directly controls the accuracy limits of autonomous AI research agents and matching engines.
■End-to-End Ownership from Investigation to CTO Decision-Making:
Work directly with complex, real-world legacy and multi-cloud data setups. You will own the full lifecycle—from data investigation and architecture design to CTO alignment and implementation.
■Direct Business Impact:
Quality improvements in company entity resolution and career history directly drive core matching rates (revenue) while minimizing critical compliance risks.
■Navigate Complex Post-Acquisition Environments:
Gain rare experience solving large-scale data engineering problems arising from combining distinct tech stacks, data models, and cross-border operations following our 2021 global acquisition of Coleman.
주요업무
Lead the entire technical lifecycle of our data foundation—from architectural design supporting search, analytics, and compliance, to entity resolution (deduplication), data cleansing, and building mechanisms for continuous data quality assurance.Detailed Scope:
■Data Platform Architectural Design:
• Design data models across multiple products and multi-cloud environments (Azure/GCP) to ensure reliable data supply for AI products, search engines, analytics, and compliance checks.
• Define Single Source of Truth (SSOT) strategies and integrate confidence scoring into data models based on empirical findings.
■Entity Resolution & Data Cleansing Pipeline:
• Design and implement multi-stage entity resolution pipelines combining deterministic matching, scoring models, and human-in-the-loop review workflows.
• Build robust data cleansing processes that enforce idempotency and state rollback capabilities to maintain high data integrity over time.
• Establish monitoring and anomaly detection mechanisms to ensure stable pipeline operations.
■Data Quality Research & Problem Solving:
• Investigate data quality issues, identify root causes within data/codebases, and formulate impactful technical solutions based on business metrics.
■Technical Decision-Making & Stakeholder Alignment:
• Document architectural designs, research findings, and Architectural Decision Records (ADRs).
• Drive consensus with the CTO, local product teams, and global engineering leaders.
＃Technical Environment
[ENS Development Common Stack]
Programming Languages: C#, Python, TypeScript
Backend Frameworks: ASP.NET Core, FastAPI
Frontend Framework: 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 Specific Tools]
Azure Data Factory
Microsoft Power BI
BigQuery
자격요건
• Data Engineering & DWH Experience: Proven track record of designing, building, and operating production-grade data pipelines and data warehouse architecture.• Relational Database Expertise: Deep hands-on experience with SQL execution plan analysis, index optimization, and high-volume batch processing.
• Root-Cause Data Investigation: Experience cross-analyzing code logic and raw data to resolve data inconsistencies, pipeline outages, and performance bottlenecks.
• Technical Documentation & Consensus Building: Strong ability to document architectural choices (design docs, research reports, ADRs) and align technical direction with stakeholders.
• Backend Development: Hands-on experience developing backend applications in modern programming languages.
• Language Proficiency: Native or bilingual level Japanese proficiency (essential for analyzing complex local data schemas and collaborating with Japanese domain teams).
・Fluent Level Japanese
・Business Level English
・Relocation to Japan required
