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.
ResponsibilitiesKey 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.
QualificationsRequired Skills
Semantic Layer Platforms
Must Have
- Cube.dev
- AtScale
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
- Redshift
- S3
GCP
- BigQuery
Multi-Cloud
- Snowflake
- Databricks
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
Skills Required
- Cube.dev experience
- AtScale experience
- Dimensional modeling
- Star schema experience
- Snowflake schema experience
- Data Vault 2.0 experience
- Entity relationship modeling
- Business vocabulary modeling
- Advanced SQL
- Python
- YAML
- JSON
- REST APIs
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field
- dbt Semantic Layer experience
- LookML experience
- Power BI Semantic Models experience
- Microsoft Fabric experience
- MetricFlow experience
- Microsoft Fabric Analytics Engineer certification
- Microsoft Azure Data Engineer Associate certification
- Snowflake SnowPro certification
- Databricks Data Engineer Professional certification
- TOGAF Foundation or Certified certification
- Master's degree
EXL Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.
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Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
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Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
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Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.
EXL Insights
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
