Responsibilities
- Develop scalable data models and transformation layers to support analytics needs
- Design reusable and well-organized data structures for analytical use cases
- Apply proven techniques in dimensional modeling and semantic layer development
- Construct data transformation workflows using modern technologies such as SQL, dbt, or similar tools
- Build and manage automated ELT processes to consolidate and transform data from diverse sources
- Ensure data pipelines operate reliably, efficiently, and at scale within cloud platforms like Snowflake
- Encourage consistency through standardized, modular, and reusable transformation logic
- Integrate testing mechanisms including data validation, schema checks, and lineage tracking to maintain data integrity
- Keep comprehensive documentation, version control, and structured development workflows
- Support the team’s adoption of current analytics engineering methodologies
- Produce and manage datasets and semantic models used by Power BI for reporting
- Verify data accuracy in Power BI reports against source models
- Enable automation and integration with Power BI via programmatic access to metadata and APIs
- Align data models with reporting outputs to ensure consistency across systems
- Convert business requirements into durable, reusable data assets
- Empower users with self-service analytics by providing clean, well-documented datasets
- Collaborate with analysts to enhance data quality and usability
- Optimize query performance and efficiency of data transformations and models
- Identify and implement automation opportunities to improve operational efficiency
- Contribute to shared tools, frameworks, and development patterns across the team
Work Arrangement
Hybrid
