Alexion

Global biopharmaceuticals focusing on rare diseases

Principal Data Engineer Lead

Data EngineeringPosted on Nov 5

Full-timeTokyo, JapanNo salary listedIn-person

About the Job

Requirements

Hands-on professional experience designing and developing data warehouses or data marts with Snowflake, including advanced SQL, data modeling, performance optimization, and enterprise analytics data architecture.
Hands-on experience with dbt for data transformation and data modeling, including designing modular, reusable data models and implementing dbt tests and documentation.
Hands-on experience with Fivetran for data integration, including designing and operating ELT pipelines, source-to-Snowflake integration, and pipeline troubleshooting.
Advanced SQL skills.
Knowledge and experience in cloud data architecture.
Practical experience with ETL and ELT architecture.
Experience with data warehouse and analytical data modeling.
Knowledge of data quality management.
Experience designing and developing production-level data pipelines.
Experience using Git for version control and collaborative development.
Ability to perform problem solving and troubleshooting.
Native-level Japanese, including the ability to independently gather requirements from Japanese business stakeholders, translate business requirements into data models and technical requirements, and explain technical topics to business stakeholders.
Business-level English sufficient to collaborate with APJ and global technical and business stakeholders.

Responsibilities

Design and build the APJ-wide enterprise data warehouse centered on Snowflake.
Design a shared data architecture for APJ markets and business functions, including a scalable and reusable medallion architecture and data layers.
Design data models that balance country-specific requirements with APJ-wide standards and build data products for use by BI, analytics, data science, and AI.
Establish engineering standards addressing performance, scalability, maintainability, and reliability.
Integrate and model commercial data domains, including customer master, sales alignment, JD Sales and secondary sales, market sales, and CRM activity data.
Integrate HCP and HCO customer master data, build customer models that account for customer IDs and relationships, and connect customer master data with CRM, sales, and alignment data.
Build models for sales organizations, territories, representatives, and customer alignment; integrate them with customer master, sales, and CRM activity data; and account for organizational and assignment changes in analytical models.
Build ingestion pipelines and models for JD Sales and secondary sales data, standardize sales transaction data, create reusable sales data products, and implement data quality checks and validation.
Integrate external market sales data into the warehouse, build common models for product and market analysis, and enable analytics combining internal and external sales data.
Integrate CRM customer interaction and activity data with customer master, sales alignment, and sales data, and build reusable datasets for customer engagement analysis.
Design and develop the Snowflake enterprise data warehouse and large-scale commercial data models; optimize SQL and data model performance; establish reusable architecture patterns and engineering standards across APJ; and build curated data products for BI, analytics, and AI.
Design and develop the dbt transformation layer and modular, reusable SQL data models; establish APJ-wide modeling standards; implement tests, documentation, and data quality checks; translate business definitions into standardized analytical models; and establish engineering practices using Git and code review.
Design and implement Fivetran ingestion pipelines from source and external data systems into Snowflake, monitor pipelines and resolve failures, onboard new APJ data sources, and build stable, maintainable ELT pipelines.
Establish data quality controls for key domains, implement automated checks and validation rules, standardize business definitions and data models, collaborate with data owners and stewards, and build a trusted data foundation for customer and commercial data.
Build analytics-ready datasets for tools such as Power BI and data products for advanced analytics and data science; design the data foundation to support future AI, machine learning, and generative AI uses; and promote self-service analytics through standardized, governed data products.

Desired Qualifications

Practical experience in the pharmaceutical or life sciences industry.
Experience with pharmaceutical commercial data.
Experience with HCP and HCO customer master data.
Experience with sales territory and alignment data.
Experience with secondary sales data.
Experience with market sales data.
Experience with CRM and customer engagement data.
Experience with enterprise BI platforms such as Power BI.
Experience building data products for AI and machine learning.
Experience with master data management, data governance, and data quality management.

ポジション概要:

Principal Data Engineer – APJ Data Warehouse は、APJにおけるエンタープライズデータ基盤の設計・構築・高度化を技術面からリードするポジションです。

Snowflakeを中核とした、スケーラブルかつ標準化されたAPJ Data Warehouse（DWH）を構築し、各市場やシステムに分散しているCommercial Dataを、信頼性が高く再利用可能なデータプロダクトとして統合します。

主な対象データドメインは以下です。

· 顧客マスターデータ

o アルトマーク等の顧客データ

· 営業アライメントデータ

o 営業組織、テリトリー、担当者・顧客アサインメント

· 実消化 / JD Salesデータ

· 市場売上データ

· CRM活動データ

o 顧客タッチポイント、CRM営業活動、Digital Engagement Dataなど

これらのデータを統合することで、Business Intelligence、Commercial Analytics、Advanced Analytics、AI が共通利用できるAPJ Data Foundationを構築します。

本ポジションはArchitectureだけを担当する役割ではなく、Snowflake、dbt、Fivetranを自らHands-onで活用できるPrincipal Engineer として活躍いただきます

主な職務内容

1. APJ Data Warehouse Architecture & Engineering

Snowflakeを中心としたAPJ共通Data Warehouseの設計・構築
APJ各Market・Business Functionで共通利用できるデータアーキテクチャの設計
メダリオンアーキテクチャ、拡張性・再利用性を考慮したData Layerの設計
各国固有の要件とAPJ共通Standardを両立するData Modelの設計
BI、Analytics、Data Science、AIから利用可能なデータプロダクトの構築
Performance、Scalability、Maintainability、Reliabilityを考慮したEngineering Standardの確立

2. Commercial Data Domain Integration

以下の主要Commercial Data Domainの統合・モデリングをリードします。

Customer Master Data

アルトマーク等のHCP/HCO顧客マスターデータの統合
Customer IDやHCP/HCO Relationshipを考慮した顧客データモデルの構築
Customer MasterとCRM、Sales、Alignmentデータをつなぐ共通データ基盤の構築

Sales Alignment

営業組織、Territory、担当者、Customer Alignment等のデータモデル構築
Customer Master、Sales、CRM Activityとの統合
組織変更・担当変更等を考慮した分析用データモデルの設計
JD Sales / Secondary Sales Data
実消化 / JD Salesデータを取り込むData PipelineおよびData Modelの構築
Sales Transaction Dataの標準化
Reporting / Analyticsで再利用できるSales Data Productの構築
Data Quality CheckおよびValidationの実装

Market Sales Data

市場売上等のExternal Market DataのDWHへの統合
Product / Market等の分析に利用できる共通データモデルの構築
Internal Sales DataとExternal Market Dataを組み合わせたAnalytics基盤の整備

CRM Activity Data

CRM上のCustomer Interaction / Activity Dataの統合
Customer Master、Sales Alignment、Sales Dataとの連携
Customer Engagement分析に利用できる再利用可能なデータセットの構築

3. Snowflake Data Platform Engineering

Snowflakeを利用したEnterprise Data Warehouseの設計・開発
大規模なCommercial Dataを対象としたData Modeling
SQLおよびData ModelのPerformance Optimization
APJ全体で再利用可能なArchitecture PatternおよびEngineering Standardの策定
BI / Analytics / AI向けのCurated Data Productの構築

4. dbt Data Transformation & Modeling

dbtを利用したTransformation Layerの設計・開発
SQLベースのModularかつReusableなData Modelの開発
APJ共通Data Modeling Standardの確立
dbtを利用したTest、Documentation、Data Quality Checkの実装
Business Definitionを標準化されたAnalytical Data Modelへ変換
Git / Code Reviewを活用したEngineering Practiceの確立

5. Fivetran Data Integration

Fivetranを利用したData Ingestion Pipelineの設計・実装
Source System / External Data SourceからSnowflakeへのデータ連携
Pipeline Monitoringおよび障害対応
新しいAPJ Data SourceのOnboarding
安定性・保守性を考慮したELT Pipelineの構築

6. Data Quality & Governance

主要Data Domainに対するData Quality Controlの確立
Automated Data Quality CheckおよびValidation Ruleの実装
Business DefinitionおよびData Modelの標準化
Data Owner / Data Stewardとの連携
Customer / Commercial Dataを信頼して利用できるData Foundationの構築

7. Analytics & AI Enablement

Power BI等から利用可能なAnalytics-ready Datasetの構築
Advanced Analytics / Data Science向けData Productの提供
AI/MLおよびGenerative AIへの将来的な活用を考慮したデータ基盤設計
Standardized / Governed Data Productを通じたSelf-service Analyticsの促進

必須要件

Technical Skills

以下の3つすべてについてHands-onの実務経験を必須とします。

Snowflake

Snowflakeを利用したData Warehouse / Data Mart設計・開発経験
Advanced SQL
Data Modeling
Performance Optimization
Enterprise Analytics向けデータアーキテクチャ設計

dbt

dbtを利用したData Transformation / Data Modelingの実務経験
Modular / ReusableなData Modelの設計
dbt Test / Documentationの実装経験

Fivetran

Fivetranを利用したData Integrationの実務経験
ELT / Data Pipelineの設計・運用経験
Source-to-Snowflake Integration
Pipeline Troubleshooting

その他の必須スキル

高度なSQLスキル
Cloud Data Architectureに関する知識・経験
ETL / ELT Architectureの実務経験
Data Warehouse / Analytical Data Modelingの経験
Data Quality Managementの知識
ProductionレベルのData Pipeline設計・開発経験
Gitを利用したVersion Control / Collaborative Development
高いProblem Solving / Troubleshooting能力

Language Requirements

日本語

Nativeレベル必須

日本のBusiness Stakeholderと独力でRequirements Gatheringを実施できること
Business Requirementを理解し、Data Model / Technical Requirementへ変換できること
技術的な内容をBusiness Stakeholderに説明できること

英語

Business-level

APJおよびGlobalのTechnical / Business Stakeholderと協働できること

歓迎要件

製薬・Life Sciences業界での実務経験
製薬Commercial Dataに関する経験
HCP / HCO Customer Master Dataの経験
営業Territory / Alignment Dataの経験
実消化 / Secondary Sales Dataの経験
市場売上データの経験
CRM / Customer Engagement Dataの経験
Power BI等のEnterprise BI Platformの経験
AI/ML向けData Productの構築経験
Master Data Management、Data Governance、Data Quality Managementの経験

Date Posted

05-11月-2026

Closing Date

30-1月-2027

Alexion is proud to be an Equal Employment Opportunity and Affirmative Action employer. We are committed to fostering a culture of belonging where every single person can belong because of their uniqueness. The Company will not make decisions about employment, training, compensation, promotion, and other terms and conditions of employment based on race, color, religion, creed or lack thereof, sex, sexual orientation, age, ancestry, national origin, ethnicity, citizenship status, marital status, pregnancy, (including childbirth, breastfeeding, or related medical conditions), parental status (including adoption or surrogacy), military status, protected veteran status, disability, medical condition, gender identity or expression, genetic information, mental illness or other characteristics protected by law. Alexion provides reasonable accommodations to meet the needs of candidates and employees. To begin an interactive dialogue with Alexion regarding an accommodation, please contact [email protected]. Alexion participates in E-Verify.

About the Company

Alexion

Global biopharmaceuticals focusing on rare diseases

Alexion, now the rare disease group within AstraZeneca, develops and sells therapies for rare and ultra-rare diseases by targeting the complement immune system. Its drugs, such as Soliris and Ultomiris, inhibit the C5 component to slow or stop disease processes, while Strensiq and Kanuma address specific metabolic disorders. It differentiates itself through a deep focus on complement biology, pursuing first- or best-in-class therapies, and using a strategy that blends internal R&D with acquisitions to expand its pipeline. Its goal is to improve outcomes for patients with rare diseases and bring these therapies to a broader international market using AstraZeneca’s scale and capabilities.

Company size: 5,001-10,000
Company stage: IPO
Headquarters: Boston, MA
Founded: 1992

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