Overview
Design and build dimensional and semantic data models, and own the AI-ready data layer at the enterprise level. Partner with governance work to ensure business and technical metadata stay aligned.
Key Responsibilities
- Design and build dimensional and semantic data models on top of the curated data layer.
- Apply software engineering practices to data transformation code.
- Own the AI-ready data layer at the enterprise for both structured and unstructured data.
- Reduce duplication and inconsistency across data models.
- Define and maintain a single source of truth for enterprise metrics and business definitions.
- Partner with Collibra-based governance work.
- Establish data contracts between upstream data producers and downstream consumers.
- Implement automated data quality tests and validation checks.
- Maintain living documentation of data models, lineage, and business logic.
- Monitor data freshness, completeness, and accuracy of consumption-layer datasets.
- Work directly with business analysts, data scientists, and product teams to understand use cases.
- Prepare and structure datasets specifically for AI/agentic consumption.
- Build or support last-mile dashboards and self-service data products.
- Act as the bridge between the Data Engineering team and business/AI consumers.
- Define and publish enterprise standards for analytical data products.
- Embed analytics standards and governance requirements into platform capabilities.
- Build and maintain shared taxonomies, reference data, business entities, and semantic relationships.
Required Experience
- 7+ years of experience in Data and/or Analytical engineering at enterprise scale
- Typically requires a Master's degree with 8 years of experience or a Bachelors degree with a minimum of 10 years of relevant experience, or equivalent combination of education and experience.
Qualifications
- SAFe or other relevant Agile certifications.
- Industry-recognized certifications in Data and Analytics Engineering, particularly Databricks certifications.
