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

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.

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

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

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.

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.