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.
• 5+ 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 customer & sales analysis.
• Familiarity with CRM systems.
• 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.
• Strong 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.
• Experience in IT product management 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.
• Take ownership of a data asset roadmap.
• Co-develop data strategy and governance frameworks.
• 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 customer data.