Senior Data Analyst

The project is in the Transport & Logistics industry and involves working within a dynamic, global organization focused on data-driven solutions.

• Minimum 6 years of experience in roles such as Mapping Analyst, Data Analyst, Data Scientist, or similar senior positions.
• Experience in agile delivery environments like Scrum, Kanban, or Spotify-inspired models.
• Ability to engage business and data stakeholders to capture business definitions, source data semantics, and transformation logic.
• Experience working closely with data engineers, solution architects, information architects, and product owners in data platform or analytics environments.
• Clear and precise English communication skills, verbal and written.
• Strong stakeholder management skills in a complex, global, matrix organization.
• Familiarity with data platforms, data warehousing/lakehouse concepts, business intelligence, and data transformation patterns.
• Experience with data profiling tools such as Databricks or similar.
• Familiarity with data quality frameworks and concepts like data cleansing, data governance, and master data management.
• Knowledge of programming languages like Python or R for data analysis (preferred).

Nice to have:

• Knowledge of cloud systems such as AWS or Azure.
• Experience in software development.

• Support squads in complex data analysis and stakeholder situations, resolving ambiguities in source data, definitions, and ownership.
• Strengthen collaboration between business, data, and engineering teams by bridging business meaning and technical implementation.
• Validate and challenge data requirements and mappings to align with the enterprise data platform vision and data mesh principles.
• Collaborate with solution architects, information architects, subject matter experts, business analysts, and squad leads to ensure data quality and semantic consistency.
• Perform in-depth data profiling and analysis to uncover patterns, anomalies, and inconsistencies.
• Develop and execute data validation and verification processes to improve data integrity.
• Create detailed reports and visualizations to communicate data profiling results and insights.