Data Analyst (f/m/d) - Quality Management - First in Quality

Do you enjoy connecting complex data across systems? Do you believe that data without action is wasted potential? Do you think new outcomes require new ways of thinking? In this role, you will help shape the future of medical technology at a leading German medical device manufacturer—ensuring world‑class quality, regulatory confidence, and patient safety on a global scale.

This is not a role for passive analysis. It is for people who challenge assumptions, turn insights into direction, and want their work to measurably improve lives.

If you want to be part of real change—rather than observe it—this is your moment.

Your Responsibilities

• Build a lifecycle “single source of truth” for AIS quality. You define and maintain a lifecycle‑oriented data model that consolidates quality‑relevant information across the entire product lifecycle. You connect and harmonize data from key functional systems such as SAP C3, Service tools, JIRA, MES and rework data to enable consistent reporting and end‑to‑end traceability. You establish and maintain data governance standards, including definitions, ownership, and quality checks, to ensure accuracy and reliability.
• Deliver a centralized dashboard and recurring reporting. You implement and continuously enhance a centralized dashboard providing visibility across Operations, Service and R&D, enabling proactive decision‑making. You prepare regular reporting and transparent progress updates for leadership and key stakeholders. You translate raw data into actionable insights related to core KPIs—such as complaint trends, work orders, or cost of non‑quality—to support preventive quality measures.
• Enable analytics that shift the organization from reacting to prevention. You perform deep‑dive analyses to identify root causes, trend patterns and early warning indicators that support preventive action planning. You contribute to predictive and proactive analytics initiatives aligned with the digital PLM vision (e.g., quality prediction, lifecycle feedback loops). You communicate insights clearly and ensure follow‑up actions are owned and executed by responsible stakeholders.
• Drive benchmarking and best‑practice identification. You support internal and external lifecycle benchmarking efforts to capture best practices and uncover improvement opportunities. You help define mandatory data sources and identify opportunities for AI‑supported analysis during the diagnostic and concept phases of the program.
• Promote cross‑functional enablement and adoption. You collaborate closely with Operations, Service, R&D and Quality teams to improve data capture practices and reduce manual, ad‑hoc data handling. You contribute to strengthening transparency, collaboration and data‑driven decision‑making across the organization.

Required Qualifications

• Degree in Data Analytics, Industrial Engineering, Quality Engineering, Informatics, Computer Science or a similar field, or equivalent professional experience.
• Proven experience in building analytics‑ready datasets using ETL, data‑warehouse models (star/snowflake) or central data‑lake structures, including data quality checks and full traceability.
• Strong ability to translate business questions into metrics, data pipelines and actionable insights, combined with clear stakeholder‑oriented communication.

Preferred Qualifications

• Advanced SQL proficiency and hands‑on experience with Python for data processing and analytics in a modern data ecosystem (ideally MS SQL, Azure Data Lake, Databricks).
• Experience working with Power BI for semantic modeling, dashboarding and interactive reporting.
• Exposure to digital PLM or digital‑thread concepts, including traceability across PLM–MES–ERP–QMS and feedback loops throughout the lifecycle.
• Understanding of regulated industries, especially medical devices, and familiarity with the relevance of traceability and compliance (e.g., EU MDR, FDA, ISO 13485).

Skills & Competencies

• Analytical rigor: ability to structure ambiguous problems, identify patterns and quantify impact.
• Data‑quality mindset: focus on accuracy, reliability, standardization and reducing manual error sources.
• Systems thinking: ability to connect product, process, service and operational data into a coherent, lifecycle‑driven view.
• Clear communication: ability to translate insights into sound decision support under operational and time pressure.

Benefits

• Mobility options, e.g., the B. Braun job ticket or job bike.
• Services to support work & family, e.g., holiday childcare.
• Flexible working hours such as flexitime and working from home.
• Employee discounts.
• Various work models, e.g., job sharing/part‑time.

B. Braun Avitum AG | Tobias Franke |