Cloud Data Modelling Engineer

PwC Poland’s Cloud & Digital team provides services in Cloud Computing, Cloud Strategy, Cloud Migrations, Data Analytics, and DevOps. They support clients in transitioning to and adopting cloud infrastructure, creating new business models, and streamlining operational activities.

• Minimum of 5 years in cloud data modelling and data engineering roles or related experience such as Business Intelligence or Data Science
• Hands-on experience with data transformation tools like Databricks, Snowflake, MS Fabric
• Experience in developing Logical Data Models into Physical Data Models using frameworks like Kimball Dimensional Modelling
• Familiarity with writing data transformation scripts using Python (PySpark) and SQL
• Proficiency in Business English at B2 level or above, including technical documentation writing
• Proven experience conducting data assessments across multiple data domains and systems of record such as ERPs and CRMs
• Experience building data warehouses, data lakes, and data lakehouses using Azure Data Factory and Azure Synapse Analytics
• Some familiarity with Master Data Management tools like Informatica, Stibo, Syndigo, Profisee, CluedIn
• Experience with new technologies and AI-based tools for task automation, information analysis, and content creation

Nice to have:

• Cloud certifications in Azure, AWS, or Google Cloud
• Data Governance certifications such as DAMA
• Evidence of training via online platforms like Coursera, Udemy, Datacamp

• Designing conceptual and logical data models and developing these into physical data models for enterprise data platforms
• Building relational, distributed, hierarchical, object-oriented, and NoSQL databases, configuring granularity, normalization, and relationships
• Creating data entity diagrams and data workflows across multiple data domains and systems to meet business user requirements
• Supporting Data Architects in designing modern data architecture for enterprise data platforms
• Collaborating with data engineers to extract, load, and transfer data from legacy systems to systems of reference
• Conducting comprehensive data assessments to identify and remediate data quality issues
• Developing expertise in automating physical data model building using tools like Azure Databricks Agentic Bricks, MS Foundry, Fabric, and Ontology
• Sharing perspectives on data modelling techniques and frameworks such as Kimball Dimensional modelling
• Developing data standards and policies around Master Data Management and Data Quality

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