Trainee (Data Management: leveraging data quality)

The European Medicines Agency (EMA) is a decentralised agency of the European Union (EU), located in Amsterdam. It is responsible for the scientific evaluation, supervision and safety monitoring of medicines in the EU.

Traineeship opportunity: Trainee (Data Management: leveraging data quality)

The Agency is looking for motivated, qualified recent graduates or students on an Erasmus+ or other programme or PhD students or Master's students (who posses a previous degree), who are interested in gaining experience and contribute to the Agency’s work for every patient in Europe. The purpose of a traineeship at the Agency is to perform tasks that are predominantly in the interest of the trainee’s training and principally serve to increase the trainee’s knowledge and to gain relevant experience.

Placement description

The European Medicines Agency (EMA) offers a traineeship opportunity in the Case Management & Data Integration Office. We are looking for a trainee in the Case Management & Data Integration Office.

Specific objectives and projects

Context

Master Data Management (MDM) forms key pillars of the European Medicines Agency's data ecosystem. These systems collect, validate, and standardise data coming from diverse internal and external sources, ensuring that high-quality product information is available for regulatory, scientific, and operational activities across the Agency and its stakeholders.

The increasing complexity of data flows – ranging from structured databases to large volumes of non-structured information – creates a growing need for better transparency, interoperability, and automated processing. As part of this environment, trainees support initiatives aimed at improving data quality, enhancing integration mechanisms, and exploring innovative technologies to transform data into actionable insights.

Project Description

During the traineeship, you will contribute to end-to-end activities across workstreams focused on analysing existing data integrations, improving data mappings and transformations, and developing proof-of-concept (PoC) solutions leveraging AI-driven techniques.

The projects typically involve two complementary dimensions:

A. Understanding and Optimising Existing Data Integrations

You will help reverse-engineer and document current system interfaces that exchange medicinal product information. This includes analysing integration architectures, understanding data exchange patterns, validating implementations against specifications, and identifying opportunities to increase transparency, robustness, and maintainability.

B. Innovation on Non-Structured to Structured Data Transformation (AI-Driven PoC)

You will work on proof-of-concepts that explore the use of AI to convert non-structured data – such as free-text content – into structured formats suitable for downstream processing and alignment with regulatory data standards.

Tasks and Responsibilities

Business & Data Analysis

• Gather and document business requirements from various stakeholders
• Analyse current business processes and data flows to understand pain points and improvement opportunities
• Perform exploratory data analysis on structured and non-structured datasets to assess formats, patterns, and quality

Technical Investigation & Solution Design

• Study existing integration architectures, message formats, validation rules, and system dependencies
• Reverse-engineer data mappings and transformations between source and target systems
• Validate implementations against technical specifications and identify gaps or inconsistencies
• Experiment with AI-based techniques to derive structured information from non-structured content

Implementation & PoC Development

• Contribute to the design and development of PoC workflows that demonstrate improved mappings, feedback loops, or AI-based processing pipelines
• Implement components that ingest, analyse, transform, or output product data for downstream use
• Work with development teams to ensure that findings can be translated into scalable future solutions

Documentation & Stakeholder Engagement

• Produce clear technical and functional documentation, including diagrams, data mapping specifications, and process descriptions
• Present findings, issues, and recommendations to business and technical stakeholders
• Contribute to reports summarising PoC results, performance indicators, risks, and proposed next steps

Learning outcomes

The trainee will have the opportunity to contribute to one of the Agency’s most important systems while working in a highly dynamic, multicultural, and interdisciplinary environment. Through hands on involvement in real projects, the trainee will gain both technical and professional skills that are highly valuable in international public sector IT environments.

During the project, the trainee can be involved in:

• Learning about the management of IT projects in public and international organisations, including governance structures, cross country collaboration, and regulatory constraints.
• Understanding and working with the MDM solution used by the Agency, including data models, system architecture, data governance concepts, and integration with downstream systems.
• Applying software engineering, development, and coding principles in the context of enterprise level systems, including version control, testing practices, and development standards.
• Gaining practical exposure to agile delivery using the SAFe framework, participating in ceremonies such as PI planning, sprint reviews, retrospectives, and daily stand ups.
• Creating architectural documentation, such as data flow diagrams, system interaction views, component descriptions, and integration specifications to support implementation and future maintenance.
• Developing problem solving and critical thinking skills, particularly in complex data and regulatory environments.
• Understanding data quality, data lifecycle management, and the role of master data in regulatory processes, gaining insight into how high quality data supports scientific and operational decision making.

Eligibility criteria

To be eligible for consideration for this placement, you are required to:

1. enjoy full rights as a citizen of a European Union Member State or Iceland, Lichtenstein and Norway;
2. possess a university degree in computer sciences, information technology, software engineering, management information system, or computer-oriented engineering (minimum of three years or more) that must have been obtained between 6 May 2025 and 6 May 2026 or be a university student on an Erasmus + or a similar programme or a PhD student or a Master's student with a previous fully finished degree in the areas mentioned before.
3. a thorough knowledge of English (at least level C1) and good knowledge of other official EU language (at least B2) of the Common European Framework for Languages

Additional skills

Technical competencies:

• Familiarity with data modelling is preferred;
• Familiarity with Cloud AI tools (e.g., Azure AI stack) is preferred;
• Familiarity with programming languages (e.g., Java, SQL) is a must.

General competencies:

• Analytical mindset;
• Able to translate technical details to non-technical audiance;
• Proactive;
• Open minded;
• Curious;
• Outcome driven.

Behavioural Competencies

You will demonstrate the following behavioural competencies:

• Communication skills
• Interpersonal skills
• Working with others
• Adaptability
• Research and analytical skills
• Learning and development

What do we offer?

• Meaningful work contributing to protecting the health of every individual and animal in Europe within multinational, multidisciplinary team
• Development opportunities (e.g. internal mobility, mentoring, peer coaching, career reflection programme, etc.)
• Work-life balance (flexible working arrangements, teleworking, generous annual leave plus official holidays)

recruitment@ema.europa.eu