Data Analyst

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Data Analyst enhancing data understanding in enterprise data platforms. Collaborating with teams to ensure data integrity and quality across domains and products.

Posted 5/4/2026full-timeHedehusene • 🇩🇰 DenmarkMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudPython

About the role

Key responsibilities & impact

Support squads in complex data analysis and stakeholder situations, helping resolve ambiguities in source data, definitions, and ownership.
Strengthen collaboration and ways of working between business, data, and engineering teams by acting as a bridge between business meaning and technical implementation.
Validate and challenge data requirements and mappings to ensure alignment with the enterprise data platform vision, data mesh principles, and overall company strategy.
Collaborate closely with solution architects, information architects, subject matter experts, business analysts, and squad leads to establish strong feedback loops that ensure end-to-end coherence, data quality, and semantic consistency.
Perform in-depth data profiling and analysis to uncover patterns, anomalies, and inconsistencies in data.
Develop and execute data validation and verification processes to document and improve data integrity and quality.
Create detailed reports and visualizations to communicate data profiling results and insights.

Requirements

What you’ll need

3-5 years of experience in roles such as Engineering manager, Mapping Analyst, Data Analyst, Data Scientist or similar senior positions.
Proven ability to engage business and data stakeholders to capture business definitions, source data semantics, and transformation logic, and translate them into precise and usable data insights.
Experience working closely with data engineers, solution architects, information architects, and product owners in data platform or analytics environments.
Good knowledge of data platforms, data warehousing or lakehouse concepts, business intelligence, and data transformation patterns.
Exposure to logistics, supply chain, or transport domains is beneficial, but not required.
Experience with data profiling tools (e.g. Databricks, or similar).
Familiarity with data quality frameworks and concepts such as data cleansing, data governance, and master data management.
Knowledge of programming languages like Python or R for data analysis (preferred).
Knowledge of cloud systems (e.g. AWS or Azure) and experience in software development is an advantage.

Benefits

Comp & perks

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Hard Skills & Tools

data analysisdata profilingdata validationdata verificationdata transformationPythonRdata warehousingbusiness intelligencedata governance

Soft Skills

collaborationstakeholder engagementcommunicationproblem-solvingfeedback loopsdata quality assuranceambiguity resolution