About the role
About Qualifyze: Founded in 2019, Qualifyze is a leading company in supply chain compliance management in the Life Sciences industry, trusted by over 1,500 pharmaceutical and healthcare companies globally. Our digital suite of solutions connects manufacturers, suppliers, and a global network of more than 250 auditors and quality experts. With a track record of over 4,500 audits across 85+ countries and the largest and most accurate supplier network and data analytics tools, Qualifyze stands as the all-in-one partner for quality compliance and supply chain risk mitigation in the Life Sciences sector. Role Overview: As an Analytics Engineer, you will be at the forefront of scaling our analytics capabilities through robust data modeling, metric consistency , and AI-powered workflows . Your goal is to bridge the gap between raw data and business insigh t, collaborating with stakeholders to understand their needs, translating them into reliable data structures, and ensuring that the metrics powering our decisions and Investor Reporting are accurate, consistent, and trustworthy. You combine a deep understanding of business logic with the technical capability to build and maintain scalable internal data models and semantic layers. Main
responsibilities:
The team member shall perform the present tasks inherent to such position, including, but not limited to, the following: Map, document, and evaluate current data models, metrics, and reporting flows. Critically evaluate existing definitions and assumptions, identify inconsistencies, and propose improvements that enable scalability and self-service analytics. Build and maintain metrics for Investor Reporting using dbt metrics , ensuring they are accurate, well-documented, and aligned with agreed business definitions. Support the modeling and maintenance of Marts and the last-mile Gold layer , contributing to a robust and scalable semantic layer that serves both operational and analytical use cases. Ensure consistency between metrics in I nvestor Reporting and in Looker , performing regular cross-checks and validations to guarantee a single source of truth . Perform data sanity checks from a business logic perspective to validate new data structures before they reach production, catching issues early and reducing downstream errors. Conduct ad-hoc analyses to support decision-making, unblock stakeholders, and surface actionable insights when needed. Help define and document business definitions , KPIs , and data processes to ensure a shared understanding across business and technical teams. Participating in projects involving agentic technologies applied to BI, including multi-agent implementations built with Claude and similar frameworks, innovate and expand what agentic analytics can deliver. Main