Business Intelligence Data Architect

What Are We Looking For?

We’re looking for a Business Intelligence Data Architect to shape the analytical architecture, data modeling standards, and engineering practices that power enterprise reporting, governed metrics, and AI-enabled analytics at Contentstack.

In this high-impact role, you’ll establish scalable, reusable modeling frameworks across Snowflake and dbt, lead the implementation of governed semantic models, and partner with Data Engineering, BI & Analytics, Revenue Operations, Finance, and executive leadership to build a trusted foundation for data-driven decision-making.

If you’re passionate about modern analytics architecture, translating complex business needs into scalable solutions, and enabling the next generation of AI-powered analytics, we’d love to hear from you! Here's what you'll be doing:

• Lead analytical architecture and governance: Define enterprise modeling standards across staging, intermediate, dimensional, fact, semantic, and mart layers. Establish conventions for model grain, naming, ownership, lineage, and documentation, and lead architectural design reviews.
• Build governed data models and semantic layers: Design and implement reusable business entities, semantic models, and enterprise metrics that support consistent reporting, self-service analytics, applications, and AI-enabled workflows.
• Establish analytics engineering best practices: Set standards for dbt development, testing, Git workflows, CI/CD, deployment reliability, model performance, and observability to improve scalability and maintainability.
• Enable secure, reliable data access: Design governed access patterns for applications, AI agents, and self-service tools consuming Snowflake data, including role-based access control (RBAC), APIs, service layers, and query optimization.
• Partner across the business: Work closely with Data Engineering, BI & Analytics, Revenue Operations, Finance, and executive stakeholders to translate business requirements into trustworthy data models and scalable analytics solutions.
• Mentor and enable the team: Guide analytics engineers on modular architecture, semantic implementation, reusable modeling patterns, and modern analytics engineering practices.
• Drive platform evolution: Evaluate tools, semantic technologies, governance capabilities, and architectural improvements that support long-term scalability while reducing technical debt.

What Really Gets Us Excited About You

• 5-8 years of experience in data engineering, analytics engineering, or data architecture, including at least 3 years in an architect capacity with direct ownership of an analytical platform.
• Deep, hands-on expertise in Snowflake architecture, including virtual warehouse design, RBAC, materialization strategies, dynamic tables, and query performance optimization.
• Expert-level experience with dbt, including staging/intermediate/mart conventions, Jinja macros, incremental models, testing, documentation, and reusable modeling patterns.
• Strong understanding of dimensional and semantic modeling, with the ability to select and justify approaches based on business and analytical needs.
• Proven experience designing and deploying production semantic layers and governed metrics across BI platforms, analytics applications, and AI-enabled workflows, including text-to-SQL and MCP-based integrations.
• Advanced SQL skills and experience with Python, along with familiarity with dbt Cloud, Fivetran, Git, CI/CD, and cloud-native analytics environments.
• Experience designing secure data 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 explain architectural decisions to technical teams, executive leaders, and cross-functional stakeholders.
• Experience collaborating with globally distributed teams across the US, Europe, and India.
• Snowflake SnowPro Core or Advanced certification; dbt Certified Data Architect or Analytics Engineer certification.
• Experience with Snowflake Cortex and Snowpark ML for in-warehouse AI/ML workloads.
• Experience working with Salesforce as a source system, including dimensional modeling for go-to-market (GTM) data.
• Experience with retrieval-augmented generation (RAG) architectures that connect large language models to governed analytical data.
• Familiarity with BI tools such as Tableau, Looker, or Power BI.
• Experience implementing data governance frameworks, including data catalogs, column-level lineage, and row-level security.
• You value a fun, collaborative, and innovative culture!