Business Analytics Architect

Muck Rack is the leading SaaS platform for public relations and communications professionals. Our mission is to enable organizations to build trust, tell their stories, and demonstrate the unique value of earned media. Muck Rack’s AI-powered, comprehensive, and integrated platform streamlines the PR workflow to help businesses generate positive media coverage, monitor mentions to manage brand reputation, and analyze PR’s impact on business outcomes. By combining media database, monitoring, and reporting into one dynamic platform, we empower teams to collaborate seamlessly, pitch effectively, and analyze results faster and more efficiently. Founder-controlled, fully distributed, and growing sustainably, Muck Rack has received several awards for its unparalleled culture and product from organizations like Inc., Quartz, G2, and BuiltIn. We value resilience, transparency, ownership, and customer devotion, and infuse these values into everything we do. We’re looking for a collaborative, self-motivated Analytics Architect to join our Business Data Analytics team. Reporting to the VP, Analytics, you’ll serve as the lead architect for Business Data Analytics, defining and driving an AI-first data and analytics architecture that modernizes our analytics platform and enables new ways for teams to access and interact with data. You’ll establish the technical blueprint for a modern data analytics stack supporting Data Ingestion to Snowflake / Databricks, Data Modeling / Semantic Layers (dbt, Python, SQL), Natural Language Analytics (Agents), Data Activation (rETL, System to System), and Self-Service Analytics (BI Tools). You’ll collaborate across Data Science, Engineering, Product, and Revenue Operations to establish scalable architecture, integration patterns, governance, and engineering practices that enable trusted data and AI-powered analyticsacross the business. What You’ll Do • Define and drive the target architecture for an AI-first enterprise data and analytics platform, establishing the technical direction and patterns needed to modernize and consolidate the analytics technology stack • Architect scalable data infrastructure, ingestion and transformation frameworks, storage models, and integration patterns across Snowflake, Databricks, dbt, Python, SQL, and related systems • Design governed semantic data layers and the supporting architecture for AI agents, natural language querying, conversational analytics, and other AI-powered data experiences • Establish architecture patterns for retrieval, APIs, and integrations that allow AI agents and applications to securely discover, understand, and interact with trusted enterprise data • Lead the evolution of data governance, security, privacy, access control, and compliance patterns across the analytics stack, balancing accessibility with appropriate safeguards • Establish and champion engineering standards for data modeling, testing, observability, CI/CD, documentation, deployment, and system integration, improving the reliability and maintainability of the analytics platform • Provide technical leadership and mentorship to the Analytics Engineers on the team To Be Set Up for Success in This Role, You’ll Need to Have • 7+ years of experience designing and evolving modern data and analytics architectures, including responsibility for architectural decisions that span multiple systems, teams, or business use cases • Deep experience with modern cloud data platforms and analytics engineering practices, including Snowflake, Databricks, dbt, Python, and SQL • Experience designing scalable data models, transformation pipelines, semantic or metrics layers, and integration patterns that enable trusted self-service analytics • Experience architecting or implementing AI-enabled data systems, such as natural language analytics, AI agents, Retrieval-Augmented Generation (RAG), vector search, or related LLM-powered data workflows • Strong knowledge of enterprise data governance, security, privacy, access control, and data quality practices, with experience translating those requirements into practical technical architecture • Experience establishing engineering practices such as automated testing, CI/CD, observability, version control, and reliable deployment patterns for data and analytics systems • Demonstrated ability to set technical direction, evaluate complex architectural trade-offs, and influence decisions across teams without relying on direct authority • Strong communication skills with the ability to translate complex data and AI architecture into clear decisions, standards, and technical direction for both technical and business partners If Any of the Below Also Describe You, This Could Be an Exciting Opportunity • You’ve helped modernize or consolidate a fragmented enterprise data ecosystem into a more cohesive modern data stack • You’ve designed semantic or metrics layers specifically to support AI-powered, conversational, or self-service analytics • You have experience taking AI agents or RAG-based data applications from architecture through production implementation • You’ve developed reusable frameworks, standards, or platform capabilities that increased the effectiveness of analytics and engineering teams • You enjoy mentoring engineers and raising technical standards while remaining an individual contributor • You’re comfortable operating in ambiguous technical environments where the architecture is still being defined and your decisions will influence how teams build for years to come In Addition, We’re Always Looking for Candidates Who • Approach architecture pragmatically, balancing immediate business needs with scalability, maintainability, security, and long-term platform health • Build alignment across technical and non-technical teams and communicate architectural decisions and trade-offs clearly • Take ownership of ambiguous, complex problems and create clarity and actionable technical direction • Lo