Enterprise Data & Analytics Architect

Resonant Inc.•Buford, GA

About The Position

The Data Analytics Architect is a senior technical leader responsible for designing, governing, and evolving the enterprise analytics ecosystem with Microsoft Fabric and Power BI as the foundational platform. This role ensures data from core enterprise systems—primarily SAP, Salesforce, Epicor, Workday, LIMS and other operational platforms—is transformed into trusted, scalable, and business-ready insights. The architect serves as the bridge between business stakeholders, data engineering teams, and analytics consumers, enabling consistent reporting, advanced analytics, and decision support across the organization.

Requirements

Bachelor’s degree in computer science, Information Systems, Engineering, or equivalent experience.
10+ years of experience in data, analytics, or BI architecture roles.
Deep hands-on experience with Microsoft Fabric and Power BI in enterprise environments.
Strong hands-on experience with S/4HANA data architecture, including CDS Views (Basic, Composite, and Consumption VDMs) and extraction patterns for Finance, Supply Chain, and HR domains.
Solid understanding of data modeling, ETL/ELT patterns, and enterprise analytics design.
Experience working with cloud-based data platforms and modern analytics tools.
Working knowledge of AI and Copilot capabilities within Microsoft Fabric, including ML pipelines, AutoML, and responsible AI principles as applied to enterprise analytics.
Practical experience applying Large Language Models (LLMs) in analytics contexts, including working with Azure OpenAI, Microsoft Copilot, and prompt engineering techniques to surface insights from enterprise data.
Ability to translate business requirements into scalable technical solutions.

Nice To Haves

Experience enabling AI and advanced analytics within Microsoft Fabric (Copilot, AutoML, notebooks, ML integrations)
Exposure to predictive analytics, forecasting, anomaly detection, and what-if scenario modeling
Strong understanding of responsible AI, model governance, and explainability in enterprise environments
Experience working in SAP-centric enterprises (Finance, Supply Chain, Manufacturing, HR)
Proven ability to translate complex analytics and AI concepts into clear business outcomes
Strategic mindset with a practical, value-driven approach
Hands-on experience integrating LLMs into analytics workflows — such as enabling natural language querying of enterprise data, LLM-assisted report generation, or AI-powered data summarisation using Azure OpenAI or Fabric Copilot
Familiarity with prompt engineering best practices and an understanding of how to govern LLM outputs within enterprise data and compliance frameworks

Responsibilities

Own the end-to-end analytics architecture—from data ingestion through semantic modeling and visualization—ensuring a scalable, secure, and governed analytics platform that supports enterprise reporting, self-service analytics, and future AI-driven use cases.
Design and maintain the enterprise analytics architecture using Microsoft Fabric (OneLake, Lakehouse, Warehouse, Data Factory, Synapse, Real-Time Analytics).
Define architectural patterns for batch, near-real-time, and historical analytics workloads.
Establish standards for performance, scalability, reliability, and cost optimization.
Architect and optimize data integration from core enterprise systems, including SAP S/4HANA, Epicor ERP, Salesforce, Workday, LIMS and other SaaS or on-prem systems.
Apply knowledge of S/4HANA Embedded Analytics and, where relevant, SAP Datasphere or BW/4HANA as complementary analytical layers alongside Microsoft Fabric.
Apply understanding of Epicor's reporting and analytics layer to ensure seamless integration into the Fabric Lakehouse and semantic model.
Define delta handling, reconciliation, and data validation strategies to ensure accuracy and trust.
Design enterprise data models (dimensional, data vault, or hybrid) aligned with business domains.
Build and govern Power BI semantic models to ensure KPI consistency, reuse, and performance.
Standardize enterprise metrics across Finance, Supply Chain, Sales, Operations, and HR.
Define Power BI architecture standards (workspace strategy, dataset reuse, Direct Lake vs Import vs DirectQuery).
Enable self-service analytics while maintaining governance and data quality.
Partner with business teams to deliver executive dashboards, operational reporting, and analytical insights.
Implement data governance across Fabric and Power BI (data lineage, certification, sensitivity labels).
Define and enforce row-level and object-level security, especially for financial and HR data.
Ensure compliance with internal controls, audit requirements, and regional data regulations.
Act as a trusted advisor to business and IT leadership on analytics strategy and roadmap.
Provide architectural guidance to data engineers, BI developers, and integration teams.
Evaluate and recommend new capabilities within the Microsoft analytics ecosystem.
Embed AI-readiness into platform architecture, enabling Copilot integrations, ML pipelines, and AutoML within Microsoft Fabric as first-class capabilities.
Define standards and patterns for predictive analytics, forecasting, and anomaly detection across key business domains such as Finance, Supply Chain, and Operations.
Establish governance frameworks for responsible AI, model explainability, and AI output trust — ensuring AI-driven insights meet enterprise audit and compliance standards.
Design and enable LLM-powered analytics capabilities within the platform architecture, including natural language querying of enterprise data, AI-assisted report generation and summarization, and secure integration of Azure OpenAI and Microsoft Copilot services — with awareness of broader LLM services accessible via Azure AI Foundry — ensuring all LLMs are grounded on governed enterprise data sources with appropriate access controls and compliance guardrails.