Power BI Data Modeler

As an ETL developer, Data Modeler specializing in Power BI, your role is pivotal in managing data within an organization. Heres a comprehensive overview of your responsibilities

Responsibilities:

1. Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).

2. Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models.

3. Define and govern data modelling and design standards, tools, best practices, and related development for enterprise data models.

4. Hands-on modelling, design, configuration, installation, performance tuning, and sandbox POC.

5. Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.

6. Should have a strong understanding of data. analysis and visualization principles and proficiency in Power BI and other data tools. Experience with SQL, DAX, and other data languages.
 Data Source Connectivity:
 Connect to various data sources (databases, APIs, flat files) within Power BI.
 Extract relevant data for analysis.
 Power Query Engine:
 Leverage Power Query as the ETL engine.
 Perform data transformations visually using Power Query (sorting, grouping, column splitting).
 Edit transformations in the M language via the Advanced Editor.
 AI Insights:
 Integrate Azure Machine Learning and Cognitive Services.
 Utilize machine learning models and cognitive capabilities for data insights.
 Reuse Across Microsoft Products:
 Recognize that Power Query is available beyond Power BI (also in Excel and SQL Server Analysis Services).
 Transfer your Power Query skills seamlessly across different tools.

Skills
 Bachelors or masters degree in data science technical or related experience.
 5+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, and ETL and data ingestion protocols).
 Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.
 Good knowledge of metadata management, data modelling, and related tools required.
 Experience in team management, communication, and presentation,