Cloud - Architect
At a glance
Mid-level Cloud role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Cloud salary
devopsroles summary, based on the employer's posting.
What you'll do
- Design semantic layers with Cube.dev or AtScale, creating reusable metrics, dimensions and measures for analytics
- Build business-friendly models over warehouses and lakehouses, aligning them with enterprise data architecture
- Set shared KPI definitions and manage metadata, lineage, catalog links, versioning and model changes
- Tune query speed with caching and aggregation strategies, improving dashboards and reports
- Work with data, BI and governance teams on design reviews, and coach engineers on semantic modeling
What you bring
- Hands-on experience implementing enterprise semantic layers with Cube.dev or AtScale
- A bachelor's degree in computer science, information technology, engineering or a related area
- Advanced SQL skills, with Python, YAML, JSON and REST APIs also listed
Who this fits
This mid-level hybrid role is based in Gurugram, India. It suits someone who can work across data architecture, analytics and governance, and mentor engineering teams on semantic modeling.
From the employer
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.
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.
Required 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
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