Data Architect

Mission: The objective of this assignment is to provide dedicated Data Architecture expertise to analyze the existing data landscape, produce its mapping and conceptual/logical data models, and work closely with internal teams to define the target data architecture and its implementation roadmap. This is primarily a Data Architecture and scoping assignment. The Data Architect will be responsible for developing data models, assessing and comparing different architectural options, documenting key decisions, and defining a concrete and actionable roadmap.

Key Responsibilities: Scoping & Alignment: Facilitate workshops with Business Analysts and the program team. Leverage existing business capabilities and processes to identify associated data objects. Define the Data Architecture principles applicable to the program.

AS-IS Data Landscape Analysis: Inventory existing data sources, including applications, repositories, databases, files, and data exchanges. Map data flows, including producers, consumers, frequencies, exchange mechanisms, and dependencies. Identify key issues such as data silos, duplication, manual re-entry, latency, data quality issues, and lack of ownership.

Data Modelling: Develop conceptual and logical data models for key business objects. Identify reference data and Systems of Record (SoR). Clarify Data Ownership rules. Produce and maintain the Data Dictionary. Define Data Quality rules.

Target Data Architecture: Assess and compare different architectural paradigms, including Data Hub, Lakehouse, Data Mesh, and Data Virtualization. Develop target architecture options in collaboration with internal architects. Define rules and guidelines for data exchange and exposure patterns, including APIs, Event-Driven architecture, replication, virtualization, and analytical data feeds. Align the target architecture with existing platforms and services.

Translate requirements related to security, personal data protection, data sovereignty, and digital sustainability into architecture principles and rules. Ensure the architecture supports future Analytics and AI use cases, particularly regarding data quality, traceability, and accessibility.

Data Governance: Define the governance foundations required to support the target architecture. Define Data Owner and Data Steward roles, governance bodies, and decision-making processes. Specify requirements for the Metadata Repository and Data Catalog. Define data access management principles. Describe the implementation of the 'Only Once' principle.

Roadmap & Transition: Break down the target architecture into coherent implementation phases. Define intermediate and transition Data Architectures. Prioritize the roadmap based on business value and risks. Identify dependencies. Ensure effective knowledge transfer to internal teams.