Description

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 up-to-date 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

- 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

📌 Cloud - Architect (Gurugram)
🏢 Important Company of the Sector
📍 Gurugram