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We are looking for a skilled Data Analytics Engineer with deep expertise in Power BI to join our team in Ankeny, Iowa. In this role, you will design, optimize, and manage semantic data models while ensuring the seamless performance of business intelligence tools. Your contributions will help drive data-driven decision-making across the organization.
Job Responsibility:
Design and implement semantic data models, including dimensional modeling and star schemas, to support business intelligence needs
Develop and optimize Power BI reports and dashboards, ensuring high performance and efficient query execution
Utilize Power Query (M) to transform and manipulate data for reporting purposes
Configure and enforce row-level security within Power BI to safeguard sensitive data
Conduct performance tuning for Power BI, including query plan optimization and refresh strategies
Collaborate with stakeholders to understand analytical requirements and translate them into actionable insights
Leverage tools such as Tabular Editor and deployment pipelines (Azure DevOps, GitHub) to streamline BI asset management
Work with cloud-based data platforms, including Databricks, Snowflake, or BigQuery, to support lakehouse architectures
Maintain adherence to enterprise BI governance practices and ensure scalable solutions for large datasets
Implement CI/CD patterns to manage semantic models and facilitate environment promotions
Requirements:
Minimum of 3-5 years of experience in analytics engineering, BI engineering, or semantic data modeling
Advanced expertise in Power BI, including semantic model design and performance optimization
Strong knowledge of dimensional modeling concepts such as facts, dimensions, and star schemas
Proficiency in Power Query (M) and row-level security configurations
Familiarity with tools such as Tabular Editor and deployment pipelines like Azure DevOps or GitHub
Experience working with cloud data platforms, such as Databricks, Snowflake, or BigQuery
Understanding of CI/CD practices for managing semantic models and BI assets
Exposure to enterprise BI governance practices and handling large datasets