Data Architect

Overview

We are currently seeking a Data Architect to join our team. This person will make data a strategic asset by developing advanced data and AI solutions to foster data-driven decision-making. Key responsibilities include shaping data strategy, data governance, data quality, and transforming data for Analytics, BI, and AI. This position will focus on SAP and Microsoft data technologies, including Azure cloud platform.

• Develop, implement, and continuously improve data governance frameworks, policies, and standards to ensure data integrity, privacy, security, and compliance across the organization.
• Define and oversee data stewardship roles, responsibilities, and processes to maintain high-quality, trusted data assets.
• Lead initiatives to classify, catalog, and manage enterprise data, ensuring data is discoverable, accessible, and protected.
• Design and manage enterprise data architecture, including data models, metadata, master data, and reference data, to support business analytics, BI, and AI initiatives.
• Establish and monitor data quality metrics, perform root cause analysis on data issues, and drive remediation efforts to ensure data is accurate, complete, and fit for purpose.
• Collaborate with business and technical teams to define data requirements, data lineage, and data lifecycle management practices.
• Assess and prepare organizational data assets for AI and machine learning use cases, ensuring data is structured, labeled, and governed for model development and deployment.
• Partner with analytics and AI teams to identify data gaps, enable feature engineering, and support data provisioning for advanced analytics projects.
• Champion the adoption of modern data platforms (e.g., Azure, SAP, cloud data lakes) and tools that facilitate scalable, secure, and AI-ready data environments.
• Architect and implement robust data solutions that align with business strategy and technology modernization goals, leveraging both on-premises and cloud platforms.
• Evaluate and recommend new technologies, tools, and processes to enhance data management, data governance, and data readiness for AI.
• Review and approve data solution designs, ensuring adherence to architectural standards and best practices.
• Create and maintain comprehensive documentation for data architecture, data flows governance processes, and data management practices.

This position is a full-time role, working from our corporate headquarters in St Louis, MO. Peabody offers flexible work policies, including hybrid office / home working arrangements.

Qualifications

• Bachelor’s degree in Computer Science, Management Information Systems or Engineering
• 10+ years of experience in enterprise-level data architecture, data integration, and data management, with a proven track record in establishing and maintaining data governance frameworks.
• 5+ years of hands-on experience designing and implementing data governance programs, including data stewardship, data quality management, metadata management, and data cataloging.
• 5+ years of experience preparing and managing data assets for Data and AI initiatives.
• 5+ years of experience with Microsoft and/or SAP BI Technologies, with a focus on data governance and data management capabilities.
• 3+ years of experience with cloud-based data solutions, including data lakes and data warehouses optimized for AI and analytics.
• Demonstrated experience in all phases of data lifecycle management: requirements gathering, architecture, design, testing, deployment, and ongoing governance.
• Proficiency in SQL, No
  
  SQL, Python, and modern data management tools.
• Experience with Microsoft Purview is a plus
• Exposure to cloud environments (especially Azure), hybrid workloads, and modern data platforms supporting AI readiness.
• Exposure to Business Intelligence tools such as Power BI and SAP Analytics Cloud.
• Exposure to data historians like CCI Cake is nice to have.

Experience

• Deep understanding of data governance frameworks, policies, and best practices, including data stewardship, data quality management, metadata management, and regulatory compliance.
• Advanced skills in enterprise data architecture, data modeling, master data management, metadata, and data cataloging to support analytics and AI initiatives.
• Ability to assess, prepare, and optimize data assets for AI and machine learning, including data labeling, feature engineering, and ensuring data quality for model development.
• Strong knowledge of cloud data architectures (especially Azure), data lakes, and integration between cloud and on-premises data sources.
• Proficiency in SQL, No
  
  SQL, Python, and modern data management and analytics tools.
• Familiarity with Dev Ops and automation processes for data operations.
• Excellent analytical skills for troubleshooting, root cause analysis, and resolving complex data issues.
• Effective interpersonal skills for cross-functional collaboration, facilitation, and negotiation.
• Ability to translate complex technical concepts for both technical and non-technical…