QUALIFICATION REQUIREMENTS FOR DATA ARCHITECT
The Data Architect candidate(s) submitted by the Contractor to provide these staff augmentation services must possess the following minimum qualifications and experience:
Minimum of 7 years of experience with large and complex database management systems.
Hands-on experience with data architecting, data mining, large-scale data modeling, and business requirements gathering/analysis.
Direct experience in implementing enterprise data management processes, procedures, and decision support.
Strong understanding of relational data structures, theories, principles, and practices.
Strong familiarity with metadata management and associated processes.
Hands-on knowledge of enterprise repository tools, data modeling tools, data mapping tools, and data profiling tools.
Demonstrated experience with repository creation and data information system life cycle methodologies.
Experience with business requirements analysis, entity relationship planning, database design, and reporting structures.
Ability to manage data and metadata migration.
Experience with database platforms, including Microsoft Azure SQL Database, Snowflake, MySQL, PostgreSQL, Microsoft SQL Server, and Oracle Database.
Understanding of Web Services (SOAP, XML, UDDI, WSDL).
Objector-oriented programming experience (e.g. using Java, J2EE, EJB, .NET, Websphere, etc).
Excellent client/user interaction skills to determine requirements.
Power BI Platform services
Experience with data processing flowcharting techniques.
Educational and Work Experience Requirements. The Contractor Data Architect, and any subsequent Contractor Data Architect provided by the Contractor, must possess, the following:
Bachelor's or Master's degree in computer science, Information Systems, or other related fields, or equivalent work experience.
A minimum of 7 years of experience with large and complex database management systems.
Expertise in technical writing and documentation.
Competency in document version control and collaborative document management.
The Data Architect candidate(s) submitted by the Contractor to provide these staff augmentation services may possess the following preferred qualifications and experience:
Experience with Florida State Agency IT Projects.
FLAIR (Florida Accounting Information Resource) data transfers and transformation for internal Department accounting systems.
PALM (Planning, Accounting, and Ledger Management) data transfers and transformation for internal Department accounting systems.
Snowflake pipeline creation
TeamDynamix iPaaS
Power BI platform services
SCOPE OF WORK
The Data Architect will report to the Application Development and Support Manager, as well as the designated Contract Manager.
Key responsibilities include:
Establishing data architecture standards, policies and procedures for the organization, including the structure attributes, and nomenclature of data elements.
Ensuring compliance and security across all data-related activities.
Supporting application rollouts and selecting appropriate tools for data management.
Managing data quality and leading data management practices.
Collaborating with cross-functional teams to drive effective data solutions.
Conducting business analysis, data acquisition, and access analysis and design.
Optimizing Database Management Systems, designing and implementing recovery and load strategies
The Data Architect applies accepted data content standards to technology projects and plays a critical role in maintaining the integrity and efficiency of organizational data systems.
The Data Architect will provide, but not be limited to, the following activities and tasks.
Strategic Planning and Vision.
Develop and deliver long-term goals for data architecture, aligning with department objectives.
Create tactical solutions and a data management roadmap to achieve strategic aims.
Establish governance processes for metadata, ensuring accuracy and validity.
Set up methods for tracking data quality, completeness, redundancy, and improvement.
Conduct capacity planning, lifecycle management, feasibility studies, and usage analysis.
Design strategies for data security, backup, disaster recovery, business continuity, and archiving.
Ensure compliance with regulatory requirements.
Acquisition and Deployment.
Support enterprise-level application rollouts (ERP, CRM, SCM, SAP, PeopleSoft, etc).
Liaise with vendors and service providers to select products/services that meet department goals.
Operational Management.
Assess and determine frameworks for data governance and stewardship.
Develop and promote data management methodologies and standards.
Select and implement tools, software, applications, and systems to support data technology goals.
Oversee mapping of data sources, movement, interfaces, and analytics to ensure quality and performance.
Collaborate with project managers and business unit leaders on projects involving enterprise data.
Address data-related problems in systems integration, compatibility, and multi-platform integration.
Lead and advocate for data management, including coaching and training with the development team.
Develop and implement testing criteria to guarantee fidelity and performance of data architecture.
Document the data architecture and environment for an accurate view of the data landscape.
Identify and develop opportunities for data reuse, migration, or retirement.
Produce documentation and contribute to Enterprise data management initiatives.
Contract Deliverables.
1.4.1. Solution Architecture Document (SAD): The primary blueprint detailing the end-to-end data platform architecture, service selection rationales, data flows, and system boundaries.
1.4.2. Architecture Decision Records (ADRs): Short, structured documents capturing key technical decisions (e.g., choosing Azure Databricks over Synapse Spark), including context, considered alternatives, and consequences.
1.4.3. Target State Architecture Blueprint: Visual diagrams created in tools like Visio or Lucidchart illustrating landing zones, compute layers, data lakes, and consumption endpoints using official Azure architecture icons.
1.4.4. Data Model Diagrams (ERDs): Conceptual, logical, and physical entity-relationship diagrams covering relational warehouses (Star/Snowflake schemas) and NoSQL collections.
1.4.5. Database DDL Scripts: Production-ready SQL scripts or Schema Definitions (JSON/Parquet/Delta) defining tables, indexes, constraints, and distribution strategies.
1.4.6. Data Lake Taxonomy & Directory Specification: A document defining the folder structures, file formats, compression standards, and partitioning strategies across ADLS Gen2 landing, staging, and curated zones.
1.4.7. Source-to-Target Mapping (STTM) Matrix: A granular spreadsheet detailing source systems, target tables, column-level transformations, business logic, data types, and refresh frequencies.
1.4.8. Pipeline Design Templates: Standardized design specs or code templates for Azure Data Factory (ADF) pipelines, Change Data Capture (CDC) frameworks, and error-handling/logging modules.
1.4.9. Interface Control Documents (ICD): Specifications outlining API endpoints, streaming topic schemas (Event Hubs/Kafka), and file exchange formats for third-party integrations.
1.4.10. Data Security & Privacy Matrix: A detailed document mapping Role-Based Access Control (RBAC), Object-Level Security (OLS), and Row-Level Security (RLS) policies to Entra ID user groups.
1.4.11. Data Protection & Encryption Specification: Documentation detailing Key Vault integrations, secret management procedures, Customer-Managed Key (CMK) configurations, and network isolation specs (Private Endpoints).
1.4.12. Compliance & Data Lifecycle Policy: Policy documentation defining data retention schedules, automated ADLS Gen2 lifecycle rules (Hot to Archive tiering), and masking requirements for PII/sensitive data.
1.4.13. Microsoft Purview Configuration & Lineage Map: An active, populated Purview data catalog featuring registered data sources, automated scan schedules, custom data classifications, and end-to-end data lineage diagrams.
1.4.14. Enterprise Data Dictionary & Business Glossary: Standardized business definitions, metadata tags, sensitivity labels, and data ownership matrices assigned within the governance platform.
1.4.15. Data Quality Framework & SLA Specs: Formal definitions of data quality rules, validation thresholds, alert thresholds, and Service Level Agreements (SLAs) for critical data assets.
1.4.16. Infrastructure-as-Code (IaC) Templates: Bicep, ARM, or Terraform scripts stored in version control for automated deployment of all data infrastructure across Dev, Test, and Prod.
1.4.17. FinOps & Cost Management Plan: A
