Data modelling and Analytics engineer

The Power BI Expert will be responsible for the following tasks:

Semantic Model Development:

Design, develop and maintain a scalable and efficient Semantic Model tailored to business requirements.

Ensure the Semantic Model supports optimal performance and user experience.

Collaborate with stakeholders to gather requirements and validate model design.

DAX Query Implementation:

Write and optimize DAX queries to create calculated columns, measures, and tables as needed.

Ensure DAX calculations are efficient and support the reporting framework.

Provide guidance on best practices for DAX usage among team members.

Databricks and Azure Databricks Integration:

Utilize Databricks Lakehouse to manage large volumes of data effectively.

Implement data processing workflows using Azure Databricks, ensuring smooth integration with Power BI.

Develop and maintain ETL pipelines using Databricks to prepare data for analysis.

DBT (Data Build Tool) Implementation:

Implement DBT for data transformation and modeling within the data pipeline.

Create modular and reusable DBT models that align with business requirements.

Collaborate with data engineers to ensure data quality and integrity.

Advanced Hands-On Experiences:

Utilize advanced Power BI features, including row-level security, incremental data refresh, and Power Query for data transformation.

Implement custom visuals and integrate APIs to extend Power BI functionality.

Develop advanced analytical solutions using AI capabilities within Power BI, such as predictive analytics and machine learning models.

Leverage advanced data modeling techniques, including star schema design, to improve query performance and simplify data analysis.

Reporting and Dashboard Creation:

Assist in developing interactive dashboards and reports based on the Semantic Model.

Implement user-friendly features that enhance data accessibility and visualization.

Performance Monitoring and Optimization:

Monitor the performance of the Semantic Model, DAX queries, and data pipelines.

Implement strategies to optimize performance as data volumes grow.