Senior Data Engineer The Hypercell team is focused on transforming cell therapy operations by streamlining supporting processes through digital, automation, and AI-driven solutions. We are seeking a Senior Data Engineer to architect and implement scalable data infrastructure that enables advanced analytics, automated reporting, and AI-powered decision-making across the cell therapy network. This role will work closely with Business Insights & Technology (BI&T) and cross-functional stakeholders to build robust data hierarchies, integrate diverse data sources, and support the deployment of innovative tools that improve operational efficiency and transparency. Key responsibilities include: Design, build, and maintain scalable data pipelines and data models to support Hypercell's digital and AI initiatives. Collaborate with BI&T and process engineering teams to define and implement data hierarchies that enable automated report generation from source systems. Integrate structured and unstructured data from manufacturing, quality, and digital platforms into unified, accessible formats. Support the development and deployment of AI-facing tools, ensuring data readiness, integrity, and traceability. Partner with data scientists and engineers to enable advanced analytics, predictive modeling, and process optimization. Ensure data governance, security, and compliance with internal and external regulatory requirements. Troubleshoot data-related issues and implement solutions to improve data reliability and performance. Contribute to the development of dashboards, visualizations, and self-service tools for end users across the business. Qualifications and experience required: Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field. 7-10+ years of experience in data engineering, preferably in biopharma, cell therapy, or regulated manufacturing environments. Strong proficiency in SQL, Python, and data pipeline frameworks (e.g., Apache Airflow, DBT). Experience with cloud platforms (e.g., AWS, Azure, GCP) and data warehousing solutions (e.g., Snowflake, Redshift). Familiarity with GMP data requirements and regulatory expectations for data integrity. Proven ability to work cross-functionally and translate business needs into technical solutions. Preferred qualifications: Experience supporting AI/ML initiatives through data engineering and model deployment. Knowledge of manufacturing systems (e.g., MES, LIMS, ERP) and integration strategies. Familiarity with visualization tools (e.g., Power BI, Tableau) and self-service analytics platforms. Strong communication skills.