Principal Analytics Architect

<p>The Principal Analytics Architect, reporting into Advantive’s Business Analytics organization, is responsible for architecting and delivering the company’s enterprise analytics data warehouse being built on Microsoft Fabric.</p> <p>This role serves as the technical owner of the analytics data foundation, defining architecture, modeling standards, and integration patterns across core business systems. Initial scope will focus on Salesforce and NetSuite, with additional systems incorporated over time. The role will also ensure the analytics platform is structured to support emerging AI-driven analytics, automation, and advanced insight capabilities across the organization.</p> <p>The Principal Analytics Architect will partner closely with Business Analytics leadership, business stakeholders, and external consultants to design a scalable, governed, and high-performing analytics platform. A critical component of this role will be replicating and modernizing existing Alteryx transformation logic into Fabric-native pipelines, ensuring continuity of business logic while improving transparency, performance, and long-term maintainability.</p> <p> The ideal candidate brings strong experience designing analytics architectures across modern data platforms and can quickly adapt to new tools and technologies. Candidates with experience in modern data platforms (e.g., Microsoft Fabric, Snowflake, Databricks) are strongly encouraged to apply.</p> <p> This role is both strategic and hands-on, balancing architectural leadership with direct contribution during the build phase of the data warehouse.</p> <p><strong>Key Responsibilities</strong></p> <p><strong>Analytics Architecture & Platform Design</strong></p> <ul style="list-style-type: disc;"> <li>Own the end-to-end architecture for Advantive’s enterprise analytics platform, leveraging a modern cloud data stack (currently Microsoft Fabric)</li> <li>Define standards for data ingestion, transformation, storage, and semantic modeling</li> <li>Design scalable, well-governed data models to support enterprise reporting and analytics</li> <li>Establish best practices for historical tracking, incremental loads, and performance optimization</li> <li>Ensure data models and pipelines are structured to support AI-enabled analytics and future advanced analytics use cases</li> </ul> <p><strong>Source System Integration</strong></p> <ul style="list-style-type: disc;"> <li>Lead integration design for Salesforce, NetSuite, and other enterprise platforms</li> <li>Ensure consistent definitions and alignment across systems and downstream reporting</li> <li>Partner with consultants and internal teams to implement reliable, well-documented pipelines</li> </ul> <p><strong>Alteryx to Fabric Modernization</strong></p> <ul style="list-style-type: disc;"> <li>Analyze existing Alteryx workflows and transformation logic</li> <li>Design scalable, platform-native equivalents using SQL, notebooks, and/or modern data pipeline tooling</li> <li>Ensure functional parity while improving maintainability and observability</li> <li>Identify opportunities to streamline or enhance transformation logic using AI-assisted development capabilities where appropriate</li> </ul> <p><strong>Analytics & Power BI Enablement</strong></p> <ul style="list-style-type: disc;"> <li>Partner closely with BI developers (e.g., Power BI) to ensure models support performant reporting</li> <li>Define semantic layer patterns that enable reuse and self-service analytics</li> <li>Support executive, operational, and financial reporting needs</li> <li>Enable responsible use of AI capabilities within the analytics platform (e.g., forecasting, anomaly detection, copilots) to enhance business insights</li> </ul> <p><strong>Governance, Quality & Standards</strong></p> <ul style="list-style-type: disc;"> <li>Define data standards, naming conventions, and documentation expectations</li> <li>Partner on data quality, lineage, and access governance initiatives</li> <li>Ensure the platform scales with business growth and acquisitions</li> <li>Support responsible AI usage by ensuring transparency, data quality, and appropriate governance controls</li> </ul>

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