- Job Role: Architecture-Cloud Architecture
- Experience (In Years): 9-12
We are seeking a highly skilled Semantic Layer Architect / Engineer to design, develop, and govern enterprise semantic layers using platforms such as Cube.dev, AtScale, and modern cloud data ecosystems.
The ideal candidate will bridge the gap between business requirements and enterprise data platforms by creating reusable business metrics, governed data definitions, semantic models, and analytics-ready data products that support BI, AI/ML, self-service analytics, and data governance initiatives.
The role requires collaboration with Data Architects, Data Engineers, BI Developers, Data Governance teams, and Business Stakeholders to establish a trusted semantic layer across the organization. Internal references highlight the importance of semantic models, governed business definitions, lineage, ontology-driven data fabrics, and enterprise semantic layers.
Responsibilities
Key Responsibilities
Semantic Layer Design & Development
- Design and implement enterprise semantic layers using:
- Cube.dev
- AtScale
- Microsoft Fabric Semantic Models
- Power BI Semantic Models
- LookML (Looker)
- dbt Semantic Layer
- Create governed business metrics and KPI definitions.
- Develop reusable dimensions, measures, and calculated metrics.
- Ensure consistency of business definitions across reporting and analytics platforms.
- Build semantic abstractions over enterprise data warehouses and lakehouses.
Data Modeling & Architecture
- Design enterprise-level:
- Dimensional models
- Star schemas
- Snowflake schemas
- Business-friendly semantic models
- Establish conformed dimensions and reusable business entities.
- Collaborate with Data Architects to align conceptual, logical, and physical models with semantic definitions.
- Define enterprise ontology and business glossary structures where applicable.
Internal architecture assets emphasize semantic data models, lineage, and enterprise semantic layer capabilities.
Analytics Enablement
- Create subject-area semantic domains for:
- Finance
- Risk
- Customer Analytics
- Marketing
- Supply Chain
- Operations
- Enable self-service analytics across BI tools.
- Simplify access to enterprise metrics for business users.
- Support executive dashboards and enterprise reporting.
Governance & Data Quality
- Establish:
- Metric governance
- Metadata management
- Data lineage
- Data catalog integration
- Partner with Governance teams to ensure trusted, auditable metrics.
- Maintain semantic model versioning and change management processes.
Internal semantic-layer discussions specifically reference lineage, shared meaning, governance, and common business definitions as foundational requirements.
Performance Optimization
- Optimize query performance and aggregate-awareness strategies.
- Design caching and acceleration mechanisms.
- Support large-scale analytical workloads.
- Improve dashboard and report performance through semantic optimization techniques.
Collaboration
- Work with:
- Data Engineers
- Data Modelers
- BI Developers
- Product Owners
- Business SMEs
- Conduct architecture reviews and solution design sessions.
Mentor engineering teams on semantic modeling best practices.
Qualifications
Required Skills
Semantic Layer Platforms
Must Have
Good to Have
- dbt Semantic Layer
- LookML
- Power BI Semantic Models
- Microsoft Fabric
- MetricFlow
Data Modeling
- Dimensional Modeling
- Star Schema
- Snowflake Schema
- Data Vault 2.0
- Entity Relationship Modeling
- Business Vocabulary Modeling
Cloud Data Platforms
Azure
- Microsoft Fabric
- Azure Synapse
- Azure Data Lake
AWS
GCP
Multi-Cloud
Programming & Query Languages
- SQL (Advanced)
- Python
- YAML
- JSON
- REST APIs
BI & Analytics Tools
- Power BI
- Tableau
- Looker
- ThoughtSpot
- Sigma
Preferred Experience
- Experience implementing enterprise semantic layers using Cube.dev or AtScale.
- Experience with governed KPI frameworks.
- Experience supporting AI and analytics platforms through semantic abstractions.
- Experience integrating semantic models with Snowflake, Databricks, Fabric, or BigQuery.
- Understanding of metadata-driven architectures, lineage tracking, and business glossaries.
Qualifications
- Bachelor's Degree in Computer Science, Information Technology, Engineering, or related field.
- Master's Degree preferred.
Preferred Certifications
- Microsoft Fabric Analytics Engineer
- Microsoft Azure Data Engineer Associate
- Snowflake SnowPro
- Databricks Data Engineer Professional
- TOGAF Foundation/Certified
Key Competencies
- Enterprise Data Architecture
- Analytics Engineering
- Semantic Modeling
- Business Metric Governance
- Data Strategy
- Stakeholder Management
Leadership & Communication
