Contract role (via consultancy Aqilea, ~10 months with extension option, on-site in Malmö) for a senior data analyst embedded at a retail client. Day to day: building enterprise Power BI reports and certified semantic models, advanced DAX, and data transformations with SQL, Python, and PySpark on Microsoft Fabric, supporting retail KPIs, reporting migration, and governed self-service analytics.
Compensation
Not disclosed
Location
Malmö, Sweden
Work setup
Not specified
Seniority
Senior
Employment
Not specified
Experience
Not specified
Category
Data analytics
Posted
Skills
agile
analytics
azure
azure-data-factory
ci-cd
data-engineering
data-lake
data-modeling
data-pipelines
data-quality
databricks
delta-lake
dimensional-modeling
git
lakehouse
microsoft-fabric
power-query
powerbi
pyspark
python
solution-design
sql
Must have
Minimum six years of relevant experience in BI, analytics engineering, or data engineering
Extensive hands-on enterprise Power BI experience: report design, workspace management, reusable/certified semantic models, governed self-service
Understanding of multi-market retail, franchisee reporting, and consistent KPI definitions
Ability to translate retail business requirements into analytical measures and communicate with business/technical stakeholders
Experience in agile, cross-functional teams with strong documentation and knowledge sharing
Willingness to contribute across two teams, with ~20–30% allocation to the secondary team (SSDP)
Comfortable in an evolving platform-building environment
Develop and maintain retail KPIs, including sales performance, transaction volumes, average transaction value, basket analysis, product performance, and comparable sales.
Improve report performance, usability, data quality, security, and maintainability.
Optimize Power BI semantic models, DAX queries, refresh performance, and Direct Lake or DirectQuery configurations.
Establish standardized reporting practices, reusable components, and common data-modeling standards.
Support legacy reporting migration, CORE-related reporting requirements, and historical data validation.
Support testing, validation, release preparation, stakeholder demonstrations, documentation, and onboarding.
Collaborate with SSDP to establish consumption-ready patterns, including Gold-layer standards, certified semantic models, and Direct Lake usage.
Support franchisee onboarding, governed self-service analytics, and report access management.
Work closely with business stakeholders to understand retail processes and ensure reporting solutions provide relevant business insights.
Share knowledge, coach colleagues, and establish reusable documentation to reduce single-person dependencies.
RequirementsMandatory
Experience: Minimum six years of relevant experience in BI, analytics engineering, or data engineering.
Power BI: Extensive hands-on experience designing and developing enterprise Power BI solutions, including report design, workspace management, reusable and certified semantic models, and governed self-service analytics.
DAX: Advanced DAX skills, including complex business calculations, filter and row context, time intelligence, calculation groups, and performance optimization.
Data Modeling: Strong experience with dimensional modeling, star schemas, fact and dimension tables, relationships, cardinality, and semantic-model optimization.
Power Query: Experience with Power Query, M language, and data transformation.
Microsoft Fabric: Hands-on experience with Fabric Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, and notebooks, or strong Power BI and Azure data-platform experience with the ability to become productive in Fabric quickly.
SQL: Strong SQL proficiency for analytical queries, data transformation, troubleshooting, and validation.
Python and PySpark: Practical experience developing data processing and transformation solutions using Python and PySpark, preferably within Microsoft Fabric or Databricks.
Data Architecture: Experience with Delta Lake, data lake architecture, Bronze/Silver/Gold data-layer patterns, and modern data platforms such as Databricks, Azure Data Lake, and Azure Data Factory.
Storage Modes: Understanding of Direct Lake, DirectQuery, and Import models, including their architectural and performance considerations.
Analytics Engineering: Experience building reusable, version-controlled, testable, and maintainable analytical assets, including Git integration, CI/CD, deployment pipelines, data-product principles, and data contracts.
Security and Governance: Experience with row-level security (RLS), object-level security (OLS), access control, and governed self-service analytics in Power BI and Fabric.
Performance and Quality: Proven experience with report and semantic-model performance optimization, data validation, and reliable analytical solution design.
BI Modernization: Experience with enterprise BI migration and reporting modernization.
Retail Business Knowledge: Good understanding of retail operations, sales and POS transactions, product and store hierarchies, markets, customer data, and reporting requirements.
Retail Analytics: Experience with retail transaction analysis, market basket analysis, sales trends, and KPIs such as net sales, gross sales, transaction count, average transaction value, basket size, product performance, and comparable sales.
Multi-Market Retail: Understanding of multi-market retail operations, franchisee reporting, and consistent KPI definitions across business entities.
Business Engagement: Ability to translate retail business requirements and terminology into reliable analytical measures and reporting solutions, and communicate effectively with business and technical stakeholders.
Collaboration: Experience working in agile, cross-functional teams, with strong documentation, communication, and knowledge-sharing skills.
Cross-Team Contribution: Ability and willingness to contribute across Business Insights and SSDP, with approximately 20–30% allocation to the secondary team as required.
Adaptability: Comfortable working in a platform-building environment where standards and processes are evolving.
Nice-to-have
Familiarity with customer loyalty, retention, segmentation, and customer behavior analytics is an advantage.
Relevant Microsoft certifications, such as PL-300 or DP-600, are an advantage.