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# Business Intelligence Data Architect

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/contentstack.md)ShareRemote from[Netherlands](https://jobicy.com/job-region/netherlands.md), [Portugal](https://jobicy.com/job-region/portugal.md)SalaryUndisclosedDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished29 Oct 2026Apply before28 Nov 2026Listing views30Application actions2Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

AI Summary

## The role, at a glance.

Contentstack is seeking a senior Business Intelligence Data Architect to define the analytical architecture supporting enterprise reporting, governed metrics, and AI-enabled analytics. The role owns scalable modeling standards across Snowflake and dbt, including semantic layers, data marts, access controls, lineage, and documentation. This architect will partner with data engineering, BI, Revenue Operations, Finance, and executive stakeholders to turn business requirements into trusted analytics products. The position also mentors analytics engineers and evolves the data platform through robust testing, CI/CD, observability, and governance practices.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThis is a high-seniority architecture role requiring deep hands-on Snowflake and dbt expertise alongside ownership of enterprise-wide data standards. Success depends on resolving complex technical and business trade-offs while influencing senior cross-functional stakeholders.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate$135,000US market range (per year)$110k–$165k0$181.50k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation was disclosed. The displayed annual USD range is an AI-estimated market range for a senior BI data architecture role and is not employer-verified.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Advanced SQL](https://jobicy.com/jobs?search_keywords=Advanced%20SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Dimensional modeling](https://jobicy.com/jobs?search_keywords=Dimensional%20modeling.md)[Semantic layer design](https://jobicy.com/jobs?search_keywords=Semantic%20layer%20design.md)[Data governance](https://jobicy.com/jobs?search_keywords=Data%20governance.md)[Role-based access control](https://jobicy.com/jobs?search_keywords=Role-based%20access%20control.md)[Data architecture](https://jobicy.com/jobs?search_keywords=Data%20architecture.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Git](https://jobicy.com/jobs?search_keywords=Git.md)[Query optimization](https://jobicy.com/jobs?search_keywords=Query%20optimization.md)[Data lineage](https://jobicy.com/jobs?search_keywords=Data%20lineage.md)[Tableau](https://jobicy.com/jobs?search_keywords=Tableau.md)[Snowpark ML](https://jobicy.com/jobs?search_keywords=Snowpark%20ML.md)

Sample interview questionsHow would you design a governed semantic layer that serves BI dashboards, self-service users, and AI applications?I would first define certified business entities, metric definitions, grains, ownership, and access policies with business stakeholders. I would implement reusable dbt models and semantic definitions, expose only governed interfaces to consuming tools, and add tests, lineage, documentation, and usage monitoring to maintain trust over time.

What Snowflake design decisions have the largest impact on performance and cost?

Key decisions include matching virtual warehouse sizes and auto-suspend settings to workload patterns, separating workloads where appropriate, choosing effective materialization strategies, and optimizing data models and query patterns. I also review query profiles, clustering needs, caching behavior, and dynamic-table refresh costs continuously.

How do you choose between dimensional modeling and other modeling approaches?

I begin with the analytical use case, required grain, update frequency, and user audience. Dimensional models work especially well for consistent reporting and reusable metrics, while normalized or wide intermediate models may be appropriate for operational transformations; the final design should preserve clarity, performance, and governed business meaning.

How would you establish quality and deployment standards for a dbt analytics engineering team?

I would define project conventions, model layers, naming rules, code review expectations, ownership, and documentation requirements. The CI/CD pipeline would run linting, compilation, unit or data tests, source freshness checks, and targeted production deployment, with observability and incident procedures for failed or degraded models.

Describe how you would secure Snowflake data for applications and AI agents without limiting legitimate analytical use.

I would use least-privilege RBAC, purpose-specific roles, governed views or semantic interfaces, and row- and column-level protections for sensitive data. Service accounts and APIs would receive narrowly scoped permissions, while auditing, query monitoring, and documented data contracts would provide accountability and support safe evolution.

Opportunity details

## About this role.

What is Contentstack?

Contentstack is redefining how modern digital experiences are built and managed. As the pioneer of the Agentic Experience Platform (AXP), Contentstack brings together structured content and brand governance (Content Cloud), real-time customer data, omnichannel personalization (Data Cloud) and autonomous AI orchestration (Agent OS) into one unified system.

While many organizations adopted headless CMS or standalone AI tools expecting transformation, they often found themselves managing disconnected systems and manual workflows. Contentstack helps enterprises move beyond that complexity by connecting content, data and AI in a way that makes digital experiences faster to launch, easier to manage and more adaptive in real time.

Leading brands including Steve Madden, LG Electronics, Subaru of America, Dolce & Gabbana, ASICS, Decathlon, and Caesars Entertainment rely on Contentstack to reduce operational friction and deliver personalized, scalable digital experiences with confidence. The company is known for its customer-first culture and commitment to the communities it serves through the Contentstack Cares program.

Learn more at [www.contentstack.com](http://www.contentstack.com/).

Who Are We?

At Contentstack we are more than colleagues, we are a tribe. Our vision is to pursue equity among our communities, employees, partners, and customers. We are global-diverse yet close; distributed yet connected. We are dreamers and dreammakers who challenge the status quo. We do the right thing, even when no one is watching. We are curious trendspotters and brave trendsetters. Our mission is to make Contentstack indispensable for organizations to tell their stories and to connect with the people they care about through inspiring, modern experiences. We care deeply about our customers and the communities we serve. #OneTeamOneDream. Chalo, let’s go!

### 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!

What Do We Offer?

Interesting Work | We hire curious trendspotters and brave trendsetters. This is NOT your boring, routine, cushy, rest-and-vest corporate job. This is the “challenge yourself” role where you learn something new every day, never stop growing, and have fun while you’re doing it.

Tribe Vibe | We are more than colleagues, we are a tribe. We have a strict “no a**hole policy” and enforce it diligently. This means we spend time together – with spontaneous office happy hours, organized outings, and community volunteer opportunities. We are a diverse and distributed team, but we like to stay connected.

Bragging Rights | We are dreamers and dream makers. Our efforts pay off and we work with the most prestigious brands, from big-name retailers to airlines, to professional sports teams. Your contribution will make an impact with many of the most recognizable names in almost every industry including AirFrance KLM, ASICS, Burberry, Mattel, Mitsubishi, Walmart, and many more!

One Team One Dream | This is one of our values, and it shows. We don’t believe in artificial hierarchies. If you’re part of the tribe, you have an opportunity to contribute. Your voice will be heard and you will also receive regular updates about the business and its performance. Which, btw, is through the roof, so it’s a great time to be joining…

To review our Privacy Policy, please click [here](https://www.contentstack.com/legal/privacy-notice-for-candidates/).

Show more

[Apply now >](https://jobicy.com/jobs/157725-business-intelligence-data-architect.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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