【AstraZeneca】【CET】Data Architect Engineering – AI-Ready Data Warehouse Lead, Data & Business Intelligence

AstraZeneca’s 2030 strategy is driven by its bold ambition to pioneer scientific innovation, lead in key disease areas,
and transform outcomes for patients worldwide. By 2030, the company aims to deliver new medicines, achieve
industry-leading growth, and set new standards for sustainability by becoming carbon negative. Positioned as a
leader in leveraging technology, data, and artificial intelligence, AstraZeneca strives to advance healthcare and create
exponential value for both patients and society. The Customer Experience & IT leads the way in shaping Japan’s
business and technology landscape, driving impactful contributions to our business success.

AstraZeneca is investing significantly in data platforms (e.g., Snowflake-based AICE-DP) and AI,
while transitioning from traditional reporting to AI-driven, interactive data applications.

New capabilities—such as Streamlit- and React-based Data analytics applications integrated with
Snowflake and power BI —enable real-time data exploration, natural language insights, and self-
service analytics. Combined with AI layers (e.g., Cortex), data can be enriched, standardized, and
transformed into insights before visualization, improving speed, governance, and decision
accuracy.

To fully enable this shift, strong metadata management and ontology (semantic layer) capabilities
are essential. These ensure that business concepts (e.g., KPIs, customer definitions) are
consistently defined and understood by both users and AI, enabling reliable, scalable AI-driven
analytics and application development.

However, a gap remains between business needs, data platforms, and AI capabilities, particularly
in translating business logic into structured, AI-ready data assets and semantic models.
This role is critical to bridge that gap by designing and operating data platforms, metadata
frameworks, and semantic data layers, translating business requirements into AI-ready data
products and applications, and ensuring that AZ’s investments are converted into scalable,
governed, and business-impacting AI use cases.

Responsibilities1. Business × Data Translation

• Understand AZ business domains (e.g., Commercial, Medical, RWD, CRM,Customer)
• Translate business requirements into data models, pipelines, and architecture
• Clearly communicate data solutions and rationale to stakeholders
• Aligned with JD expectation: strong business understanding + logical communication

2. Data Engineering Execution