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.

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.

• Languages: Python, SQL
• AI/ML & Orchestration: Azure OpenAI, Gemini, Anthropic, LangChain, LangGraph, Scikit-learn, Hugging Face Transformers
• Backend & Web: FastAPI, Streamlit, SQLAlchemy
• Data & Search: Azure Cognitive Search (AI Search), Elasticsearch, Redis, Azure Cosmos DB, PostgreSQL
• Infrastructure & DevOps: Docker, Kubernetes, Azure Functions, Azure DevOps
• Project Management & Collaboration: Slack, Google Meet, Jira, Confluence, esa.io
• AI Coding Assistants / Developer Tools: Claude Code, Devin, ChatGPT

【Data Platform Engineer】
Azure Data Factory
Microsoft Power BI
BigQuery

■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.

#1: Screening / Casual Interview
#2: 1st Interview
#3: 2nd Interview:
#4: Final Interview

【Annual Salary Range】
¥7,000,000 to ¥12,000,000

• Salary details will be determined based on skills and experience.

月給：538,000円 〜 882,000円
基本給：412,000円 〜 676,000円
固定残業代：126,000円 〜 206,000円
（固定残業手当30時間/固定深夜手当40時間含む）

正社員

入社後3ヶ月（本採用と同条件）

時間管理または専門業務型裁量労働制
※スキル、ご経験、担当業務、および社内制度の改定等によって、勤務体系は決定させていただきます。

時間管理
10:00～19:00
所定労働時間：8時間00分／休憩：60分

専門業務型裁量労働制
1日のみなし労働時間8h／休憩1h
平均労働時間：160時間/月
（対象期間2022年下期 ・開発組織メンバーの平均）

• 通勤手当（上限あり）
• 仕事のパフォーマンスをあげるための福利厚生（身体のメンテナンス、家事代行、学習費用補助等に月1万円まで使用可）
• 外部セミナー参加補助
• 書籍購入にかかる費用負担
• インフルエンザ予防接種
• 健康診断費／健康診断オプション費用負担補助
• 健康保険加入に伴う福利厚生（保養施設やスポーツ施設の利用・飲食店の補助等）
• 借上社宅制度
• 遠方からの転居費用補助（採用時のみ）
• 勉強会・社内懇親会にかかる費用補助
• 社内部活動の活動費補助
• 新入社員歓迎・社内交流ランチ費補助

健康保険・厚生年金・雇用保険・労災保険

屋内禁煙、喫煙室なし
敷地内全体喫煙禁止

【業務範囲について 】
雇入れ直後：上記を参照ください
変更の範囲：当社における各種業務全般