Data Architect

W.C. Bradley Co.•Columbus, GA

16d

About The Position

The Data Architect designs, develops, and governs enterprise data architecture to enable reliable, secure, scalable, and accessible data across the organization. The role establishes the frameworks, standards, models, and integration patterns for how data is collected, structured, stored, integrated, governed, and consumed across business applications and analytics platforms. The Data Architect is a strategic and technical partner to business leaders, IT, application teams, data and analytics teams, and external partners. The role supports an environment involving multiple business units, brands, locations, ERP platforms, and acquired or integrated businesses. It balances long-term enterprise architecture with practical business needs to improve data quality, simplify the technology landscape, strengthen governance, and establish a trusted foundation for reporting, analytics, automation, and artificial intelligence.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, or a related field.
8+ years of progressive experience in data architecture, data engineering, enterprise architecture, application architecture, or related disciplines.
3+ years of experience performing enterprise- or solution-level data architecture responsibilities.
Demonstrated experience designing data architectures across complex enterprise technology environments.
Strong knowledge of relational and dimensional data modeling, data integration, master data, metadata, data lineage, and data governance to enable enterprise-level artificial intelligence capabilities.
Experience with modern cloud data platforms, data warehouses, data lakes/lakehouses, ETL/ELT, APIs, and integration technologies.
Experience with platforms such as Microsoft Azure, AWS, Snowflake, Databricks, Microsoft Fabric, or comparable technologies.
Experience with Power BI or similar enterprise analytics platforms.
Demonstrated ability to translate business requirements into scalable technical architecture.
Experience working across multiple functions, systems, and business processes.
Ability to understand interdependencies among business processes, applications, data, and technology and make enterprise-wide decisions.
Expertise in data modeling, integration, platforms, governance, and modern data architecture.
Ability to connect architecture decisions to operational and financial outcomes, and balance immediate project requirements with long-term architectural objectives.
Ability to build alignment across technical and non-technical stakeholders and challenge decisions that introduce unnecessary complexity or risk.
Ability to diagnose complex data problems, identify root causes, evaluate alternatives, and recommend sustainable solutions.
Ability to communicate technical concepts clearly to executives, business leaders, project teams, and technical professionals.
Ability to convert architecture strategy into practical standards, designs, roadmaps, and implementation requirements.

Nice To Haves

Experience with SAP S/4HANA and SAP data structures, integration, or master-data environments.
Experience in manufacturing, consumer products, retail, supply chain, distribution, or similarly complex operating environments.
Experience integrating data following acquisitions or across multiple business entities and technology environments.
Experience establishing or improving enterprise data governance and master-data-management programs.
Familiarity with AI/ML data architectures and data-readiness requirements.
Relevant certifications in cloud architecture, data management, SAP, TOGAF, DAMA/CDMP, or related disciplines.

Responsibilities

Develop and maintain enterprise data architecture strategy, standards, principles, and roadmaps aligned with business and technology objectives.
Design scalable data architectures across cloud, on-premise, and hybrid environments for structured and unstructured data.
Define current- and future-state architectures, reduce complexity and technical debt, and establish patterns for data ingestion, storage, integration, transformation, consumption, and retention.
Evaluate emerging technologies and ensure architecture decisions support analytics, AI, machine learning, and automation.
Develop and govern conceptual, logical, and physical data models for enterprise applications, reporting, analytics, and integrations.
Establish enterprise data standards, definitions, canonical models, metadata, lineage, retention, and lifecycle management practices.
Partner with business and technical teams to identify authoritative data sources and ensure models support operational and analytical needs.
Provide architecture leadership for SAP S/4HANA and other enterprise platforms, including finance, HR, CRM, manufacturing, supply chain, and e-commerce systems.
Define integration standards and data exchange frameworks across applications, acquired businesses, legacy systems, and external partners.
Support implementations, migrations, conversions, and transformation initiatives while ensuring cross-functional business processes are integrated.
Define data ownership, stewardship, and accountability, and implement controls for data quality, consistency, accuracy, completeness, and timeliness.
Develop processes to identify and resolve systemic data issues and maintain enterprise metadata, lineage, data catalogs, and business glossaries.
Define architecture standards for APIs, data pipelines, ETL/ELT processes, warehouses, lakes, and analytics platforms.
Collaborate with reporting and analytics teams to make data structured, governed, and accessible for enterprise reporting and self-service analytics.
Promote reusable integration patterns and reduce redundant data repositories and point-to-point integrations.
Establish data architecture requirements for AI, machine learning, predictive analytics, and generative AI initiatives.
Assess data readiness for business use cases and ensure AI solutions use secure, governed, accurate, and traceable enterprise data from multiple sources.
Collaborate with cybersecurity, legal, privacy, and compliance teams to incorporate security and regulatory requirements into data architecture.
Define standards for data classification, access, encryption, retention, and disposal to protect sensitive information throughout its lifecycle.
Translate complex technical concepts into clear business recommendations, risks, and opportunities.
Facilitate architecture governance, standards reviews, and decision-making across business and technology teams.
Partner with project teams throughout solution delivery and guide stakeholders toward decisions supporting the long-term enterprise data strategy.