Requirements

Expected technologies

Azure Databricks

SQL

Python

PostgreSQL

MSSQL

Our requirements

• Master's degree in Statistics, Computer Science, Data Management, Data Science or a related field.
• 3+ years of experience as a Data Analyst or Data Steward, preferably within the consumer-packaged goods, FMCG, pharmaceutical or healthcare industry.
• Strong knowledge of data management principles, data quality frameworks, and metadata management practices and tools.
• Understanding of data lineage and data cataloging concepts.
• Business acumen in the area of product master data.
• Familiarity with master data management tools (e.g. Reltio Cloud MDM, Syndigo Enterprise Data Suite), SAP modules related to master data management (e.g. Material Management, Product Lifecycle management) and master data aspects of the CRM systems (e.g. IQVIA OCE-P, Salesforce).
• Experience with data manipulation and analysis using Azure Databricks, SQL and Python.
• Familiarity with relational databases (PostgreSQL, MSSQL) and data modelling.
• Excellent analytical and problem-solving skills with a keen attention to detail.
• Understanding about data compliance & security standards such as data privacy regulations (GPDR, HIPAA), EU AI Act, management of confidential data, and experience with measures to mitigate data risks.
• Strong communication skills, with the ability to present complex data in a clear and understandable manner.
• Interest and experience with AI tools supporting data analysis and stewardship is a plus.
• Experience with preparing data for AI solutions (e.g. traditional machine learning models, AI Agents) is a plus.
• Ability to work collaboratively in a team-oriented environment.
• Fluent in English, both written and spoken.

Your responsibilities

• Collaborate with data owners, architects, engineers, stewards, governors, scientists and product managers to understand objectives and requirements for data assets.
• Serve as a liaison between technical teams and business stakeholders to ensure data assets meet both business requirements and technical standards.
• Proactively identify new datasets from across the organization and collaborate with data architects and data engineers to integrate them into the core data assets
• Define transformation logic and enrich data to create valuable KPIs and features in the consumption layer.
• Develop and maintain clear semantics and metadata for data assets, ensuring that all data is well-documented and easily understandable.
• Ensure data quality, availability, and completeness through implementation of quality checks, validation processes, and continuous monitoring in collaboration with data architects and data engineers.
• Serve as the primary point of contact for users of data assets, providing guidance and support to help them understand and utilize the data effectively.
• Analyze complex datasets to extract actionable insights that streamline the development of analytics and AI solutions.
• Become a go-to expert for master data.