Data Architect C – Advanced - 26-00301

NavitsPartnersTallahassee, FL
1d

About The Position

The Data Architect – Advanced will support enterprise data modernization initiatives focused on scalability, governance, security, and analytics enablement. This role is responsible for designing and implementing a comprehensive data integration framework that unifies enterprise data assets across applications, databases, automated processes, and reporting systems. The successful candidate will lead efforts to transition legacy and siloed data environments into a connected, governed, cloud-aligned data ecosystem. The framework will support standardized schemas, interoperable data flows, and analytics-ready data assets while adhering to “collect once, use many” principles.

Requirements

  • Minimum 7 years working with large, complex database management systems
  • Minimum 7 years as a Data Architect or Senior Data Analyst
  • 10+ years working with enterprise databases and data integration tools
  • Strong experience with financial or enterprise data systems
  • Proven expertise in data modeling, ETL development, and data governance
  • Experience with cloud-based data platforms and data warehousing solutions
  • Familiarity with data visualization and reporting best practices
  • Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field
  • Relevant work experience may substitute for education on a year-for-year basis

Nice To Haves

  • Experience with legacy database platforms
  • Hands-on experience with modern data integration tools
  • Strong analytical skills with the ability to translate business needs into technical solutions
  • Excellent written and verbal communication skills

Responsibilities

  • Current State Assessment
  • Review existing systems to identify data silos, schema inconsistencies, and governance gaps
  • Engage stakeholders to gather and document enterprise data integration requirements
  • Analyze and validate existing data models to assess modernization impacts
  • Evaluate dependencies and scope changes required for modern data architecture
  • Conduct impact analysis on reporting and application usage to ensure business continuity
  • Framework Design & Architecture
  • Define enterprise data architecture strategy, including canonical models, APIs, and ontologies
  • Map integration points across cloud platforms, data warehouses, integration tools, and databases
  • Develop conceptual, logical, and physical data models for key enterprise domains
  • Redesign and standardize data schemas to ensure scalability and robustness
  • Ensure compatibility with cloud-first architecture strategies
  • Support spatial and GIS data integration
  • Data Governance & Quality
  • Implement data governance structures, metadata standards, and stewardship roles
  • Design and enforce data quality checks for accuracy, timeliness, and completeness
  • Establish metadata management, lineage tracking, and stewardship processes
  • Collaborate with development teams to automate and optimize ETL workflows
  • Data Integration & Interoperability
  • Build ETL/ELT pipelines and real-time data ingestion frameworks
  • Enable enterprise-wide data exchange across internal and external systems
  • Ensure consistent enterprise identifiers across applications
  • Implement orchestration solutions using modern cloud and integration platforms
  • Design RESTful APIs for engineering and spatial datasets
  • Technical Leadership & Strategy
  • Partner with technology leadership to define data integration strategies and roadmaps
  • Provide architectural guidance on tools, platforms, and technical standards
  • Act as a technical mentor, advisor, and change agent across teams
  • Support continuous improvement initiatives related to enterprise data management
  • Key Deliverables
  • Current-state assessment documentation for enterprise data models
  • Enterprise data models, schemas, architecture diagrams, data dictionaries, and API specifications
  • Documentation on data quality controls and ETL enhancements
  • Data governance framework and metadata standards
  • Phased implementation roadmap with milestones and deliverables
  • Implementation playbooks and training materials
  • Regular project status updates and stakeholder presentations
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