Lead the end-to-end design and implementation of enterprise data platforms for a global engagement in the pharmaceutical and life sciences sector. The role focuses on scalable data architecture and engineering, including Snowflake platforms and dbt pipelines.
Strong hands-on expertise in Snowflake, including data warehousing, performance tuning, security, and cost optimization.
Deep understanding of data warehousing concepts, including dimensional modeling and star/snowflake schemas.
Advanced proficiency in SQL and complex query optimization.
Extensive experience in ETL/ELT development, especially with dbt.
Experience in data architecture and distributed data systems design.
Strong exposure to CI/CD pipelines, including Git, Jenkins, GitHub Actions, and Azure DevOps.
Experience working with cloud platforms, including AWS.
Strong stakeholder management and communication skills across business and technical teams.
Ability to drive architecture decisions and influence enterprise data strategy.
Experience working in Agile/Scrum environments.
Nice to have:
Experience with orchestration tools such as Apache Airflow or similar.
Exposure to data lake or lakehouse architectures.
Experience with data visualization tools such as Tableau and Power BI.
Prior experience in the pharmaceutical or healthcare domain, including clinical, R&D, or commercial data.
Platform Architecture & Delivery:
Lead the end-to-end architecture, design, and implementation of scalable, high-performance data platforms built on Snowflake.
Standards & Best Practices:
Define, establish, and enforce enterprise data architecture standards, including data modeling, naming conventions, design patterns, and engineering best practices.
Modern Pipelines & ETL/ELT:
Design and implement maintainable ETL/ELT pipelines using dbt and modern cloud-native technologies.
Global Stakeholder Engagement:
Collaborate with business leaders, data scientists, clinical domain experts, and IT teams to translate business requirements into technical architectures.
Data Governance & Quality:
Implement data governance, lineage, metadata management, cataloging, and automated data quality frameworks.
Regulatory Compliance & Security:
Ensure data platforms comply with European data protection regulations, pharmaceutical compliance, validation, and security guidelines.
DataOps & Automation:
Implement DataOps methodologies and CI/CD pipelines for automated testing, deployment, workflow orchestration, and platform monitoring.
