JustJoin.IT Hybrydowo Senior New

Senior Manager Data

Link Group

⚲ Warszawa

Wymagania

  • Data
  • Business Intelligence
  • AWS
  • Snowflake
  • Databricks
  • SQL
  • Python

Opis stanowiska

Senior Manager Data Summary: The Senior Manager Data is responsible for overseeing the operational and technical management of the organization’s data assets. This role focuses on building and maintaining robust data pipelines, ensuring data quality, and implementing governance standards that support advanced analytics and business intelligence. You will lead a team of data professionals to ensure that our data infrastructure remains scalable, secure, and ready to meet the demands of a modern Life Science organization. Key Requirements: • Proven professional experience within the Life Science industry, specifically working with clinical or laboratory data and understanding GxP data integrity principles (ALCOA+). • Minimum of 8 years of experience in data management, data engineering, or business intelligence, with at least 4 years in a management or lead role. • Strong technical expertise in modern data architectures (e.g., Snowflake, Databricks, or AWS Data Lakes) and proficiency in SQL, Python, or data orchestration tools. • Demonstrated experience in implementing data governance and data privacy controls in compliance with global regulations such as HIPAA and GDPR. • Excellent English communication skills, with the ability to manage technical teams and communicate data health and project status to senior leadership. Main Responsibilities: • Oversee the design, development, and maintenance of enterprise-scale data pipelines and integration layers. • Ensure all data management practices are fully compliant with Life Science regulatory requirements and internal quality protocols. • Manage a team of data engineers and analysts, setting clear technical goals and ensuring the delivery of high-quality data products. • Collaborate with AI and Research teams to provide clean, structured, and validated datasets for advanced modeling and discovery. • Drive the continuous improvement of data quality through automated monitoring, cleansing processes, and proactive metadata management.