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.

Responsibilities

1. 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
• Design, build, and maintain ETL/ELT pipelines
• Develop data solutions using Snowflake, dbt, Fivetran, etc.
• Ensure data quality, consistency, and reliability
• Core Data Engineer responsibilities)

3. AI-ready Data Platform Development
• Design data infrastructure optimized for AI/ML use cases
• Enable scalable, real-time, and high-performance data processing
• Provide high-quality datasets for AI, advanced analytics, and Agentic AI
•AI-ready data foundation is a strategic focus area

4. Innovation & New Technology Adoption
• Evaluate and implement new technologies (AI / ML / GenAI)
• Conduct PoC (Proof of Concept) and scale successful use cases
• Continuously improve data platforms and capabilities
• Innovation and experimentation expected

5. Collaboration & Leadership
• Collaborate with Data Scientists, Advanced Analytics, Analysts, Business stakeholders
• Work with external partners/vendors (e.g., ZS, TCS, AZ Global DAPS)
• Mentor junior engineers and contribute to engineering standards
• Cross-functional collaboration and leadership required

Qualifications

Required:
• 10+ years of experience in data architecture, data modeling, and database design. Experience with large-scale
data warehousing and data lakes is essential.
• Experience in the pharmaceutical industry or in a related field where data sensitivity and compliance are
critical.
• Strong communication and interpersonal skills to collaborate with various stakeholders and to translate
business needs into architectural solutions.
• Ability to analyze complex data and system requirements and design comprehensive data architecture
solutions.
• Proven experience in designing and implementing enterprise-scale data warehouses.
• Strong knowledge of data modeling, ETL/ELT processes, and distributed systems. Like dbt,fivetran
• Hands-on experience with cloud platforms (Snowflake, AWS RDS,redshift ,oracle) and modern data stack
tools.
• Familiarity with AI/ML workflows and how data architecture supports them.
• Excellent communication and leadership skills.
• Communication with English for global stakeholders.

Preferred:
• Certifications in data architecture, data management, or related cloud data services (e.g., AWS Certified Data
Analytics, Snowflake Data Engineer).
• Proven experience leading and mentoring data architecture teams and managing large-scale and complex
projects.
• Experience with the latest technologies in big data, machine learning, business intelligence, and real-time
analytics.
• Proficiency in project management methodologies and tools.
• Strong understanding of business processes and strategic planning, with the ability to align data architecture
with business objectives.

Location

• Osaka

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.