About the role
What Are We Looking For?
We are seeking a BI Data Architect to lead the analytical modeling architecture and engineering standards that power reporting, semantic modeling, and AI-enabled analytics across the enterprise. In this high-impact role, you will establish scalable modeling patterns and reusable frameworks across Snowflake and dbt while partnering with leadership to operationalize governed metrics and long-term analytics strategy through architectural implementation best practices.
Key Responsibilities
Architectural Leadership and Governance
● Define and govern enterprise analytical architecture standards and scalable modeling frameworks across all layers (staging, intermediate, dimensional, fact, semantic, and mart layers), leveraging Snowflake and dbt.
● Establish standards for model grain declaration, naming conventions, ownership metadata, lineage, and documentation.
● Lead architectural design reviews for new modeling initiatives and analytical platform changes.
● Partner with Data Engineering teams to align ingestion patterns and source system architecture with downstream analytical requirements.
Data Modeling and Semantic Layer
● Lead the design and technical implementation of governed business entities, semantic models, and enterprise metrics in partnership with BI andAnalytics.
● Design, implement, and enforce secure access patterns (APIs, RBAC, query design) for
applications, AI agents, and self-service tools consuming Snowflake data through governed
semantic models.
● Translate complex business requirements from Revenue Operations, Finance, and executive leadership into scalable, trustworthy data models and BI solutions.
Analytics Engineering and Platform Enablement
● Establish and enforce scalable analytics engineering standards for dbt development, testing, deployment workflows, Git practices, CI/CD, model performance, observability, and deployment reliability.
● Partner with BI Data Engineering teams to improve model consistency, modularity, scalability, and long-term maintainability.
● Mentor analytics engineers on modular architecture, semantic implementation, reusable modeling patterns, and modern analytics engineering best practices.
● Evaluate and recommend tooling, governance capabilities, semantic technologies, and platform improvements that support scalability and reduce technical debt.
Required Qualifications:
● 8+ years in data engineering, analytics engineering, or data architecture roles, with at least 3 years operating in a senior or lead architect capacity with direct analytical platform ownership.
● Deep, hands-on expertise with Snowflake architecture, including virtual warehouse design, RBAC, materialization strategies, dynamic tables, and query performance optimization.
● Expert-level dbt Labs practitioner with experience in staging/intermediate/mart conventions, Jinja macros, incremental strategies, testing frameworks, documentation, and reusable modeling patterns; dbt Certified Data Architect or Analytics Engineer certification preferred.
● Demonstrated experience designing and deploying production semantic layers serving governed metrics across BI platforms, analytics applications, and AI-enabled analytics workflows, including text-to-SQL and MCP-based analytics integrations.
● Strong command of dimensional and semantic modeling concepts, with the ability to select and justify the appropriate modeling approach based on business and analytical requirements.
● Advanced SQL proficiency and experience with Python; familiarity with modern analytics tooling including dbt Cloud, Fivetran, Git/CI-CD workflows, and cloud-native analytics environments.
● Experience designing secure access patterns for applications consuming Snowflake data, including APIs, service layers, RBAC, query performance, and governed semantic access.
● Strong written and verbal communication skills with the ability to present architectural decisions to executive and cross-functional stakeholders.
● Snowflake SnowPro Core or Advanced certification strongly preferred.
Preferred Qualifications:
• Snowflake Cortex and Snowpark ML experience for in-warehouse AI/ML workloads.
• Experience with Salesforce as a source system, including GTM dimensional modeling from CRM objects.
• RAG architecture experience connecting LLMs to governed analytical data foundations.
• Experience working with BI tools such as Tableau, Looker, or Power BI.
• Experience with data governance frameworks including data catalogs, column-level lineage, and row-level security.
Experience: 10-15 years
Location: Bangalore/Chennai/Pune/Virar