The RoleVeeva Link’s vision is to become the global leader for deep data in life sciences. We are looking for a detail-oriented and experienced Data Analyst to join our product team.In this role, you will play a key part in developing and implementing a data governance framework, ensuring data accuracy, completeness, and integrity. Your main responsibilities include establishing data quality monitoring processes, identifying root causes of inconsistencies, and collaborating with cross-functional teams to resolve data gaps.To thrive in this role, you need expertise in SQL and Python for data manipulation and automation. You should be comfortable working with large datasets, designing dashboards with actionable recommendations, and applying statistical techniques to assess and enhance data quality.The idóneo candidate has experience in creating documentation (process documentation, data collection guidelines)



and in contributing to data governance policies and best practices.Implement a data governance frameworkEstablish and maintain robust processes for tracking and assessing data qualityDevelop automated checks to proactively detect data inconsistenciesApply statistical techniques to enhance data qualityCreate customized reports and dashboardsExtract data from databases (e.G., Redshift, MongoDB)Identify and resolve root causes of quality issuesCollaborate in cross-functional teams of product managers, engineering, and operationsCreate and maintain documentation5+ years of work experience in data analysis or data quality roleStrong technical skills in data analysis, with proficiency in SQL and PythonExperience in data storage and processing on cloud platforms such as AWS, GCP, or AzureStrong problem-solving skills and experience working with cross-functional teamsExperience writing process documentation and data collection guidelinesUniversity degree in medicine or life sciencesUnderstanding of healthcare, life sciences, or pharmaceutical industry dataExperience with

📌 Senior Data Analyst (Madrid)
🏢 Veeva
📍 Madrid