[Remote] Senior Data Engineer
Note: The job is a remote job and is open to candidates in USA. Agilyti is a Data and AI Consulting firm seeking a Senior Data Engineer with expertise in dimensional modeling and OLAP to build and validate the AtScale semantic layer for client implementations. The role involves designing and constructing multidimensional semantic models that will define how businesses consume and measure their data.
Responsibilities
- Configure the data warehouse service account and schema permissions AtScale requires (SELECT, UPDATE, CREATE, DROP on aggregate and UDAF schemas)
- Connect AtScale to the client’s cloud data warehouse
- Design and build multidimensional semantic models — dimensions, hierarchies, metrics, KPIs, and time intelligence
- Translate existing business logic, reports, and KPI definitions into governed semantic model objects
- Configure query protocols per BI tool (DAX for Power BI, MDX for Excel, SQL for Tableau / Looker)
- Validate model accuracy against existing reports and dashboards
- Optimize aggregate configurations for query performance
- Establish naming conventions, governance policies, and model documentation standards
- Set up Git-based version control for semantic models and support CI/CD pipeline integration
- Partner with the BI Integration Specialist (or end users) to validate the consumer experience
Skills
- 5+ years in data engineering or BI development focused on dimensional / OLAP modeling
- Hands-on with at least one of: SSAS, Essbase, AtScale, Cube, or LookML
- Strong SQL plus a cloud data warehouse (Snowflake, BigQuery, Redshift, or Databricks)
- Working knowledge of MDX and/or DAX
- Designing fact/dimension schemas, hierarchies, and KPI frameworks
- Data warehouse permissions and service account configuration
- Git-based version control for data models or transformation code
- Direct AtScale implementation experience
- Migrating legacy SSAS / on-prem OLAP cubes to cloud platforms
- Dbt, LookML, or other semantic / transformation modeling tools
- Python for AI/ML data access use cases
- Configuring ODBC, JDBC, or XMLA client connections
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