Data Engineer - Databricks/ETL/PowerBI/Tableau/Azure

Requirement:

. Years of related professional experience: Minimum 5 years.

. Educational position requirements: A formal education and subsequent undergraduate/graduate degrees in related field are nice to have

. Excellent communication skills, able to collaborate effectively and influence across several stakeholder groups.

. Business acumen preferably developed as part of a Supply Chain business domain.

. Hands-on experience building BI solutions end-to-end: requirement gathering, datamodelling , ETL development and dashboard delivery in a professional setting.

. Proficiency with the CLIENT'S analytics tech stack: Azure (cloud platform), Azure DevOps (ADO) for task and pipeline management, Databricks for data engineering and exploration, Tableau Prep KNIME for ETL, and Tableau and Power BI for visualization.

.Experience in dimensional data modelling (star schemas) and building semantic layers that support self-service analytics.

. Practical experience with ETL/ELT tooling - Tableau Prep, Azure Data Factory, or equivalent - to build and maintain data transformation pipelines.

Roles & responsibilities:

. Advanced BI engineering practices spanning dimensional data modelling, DAX/LOD optimization, ETL pipeline design and semantic layer architecture within CLIENT'S's digital environment.

.Deep knowledge of CLIENT'S's Supply Chain data ecosystem - including source systems, Databricks data architecture, certified datasets, ETL patterns and platform governance standards.

. Frameworks and techniques for requirement gathering, AI-assisted data exploration, automated pipeline documentation and prompt-driven analysis of complex data products.

. CLIENT'S's data governance standards, field certification processes and scalable BI engineering practices across Tableau Prep, KNIME, Databricks, Tableau and Power BI.

. Collaboration within a global matrixed organization - working alongside data engineers, product owners, analytics teams and vendor partners.

. Skills to grow into senior BI engineering, data product or analytics engineering roles with broader platform ownership and architectural responsibilities over time.