Data Architect

·        Should be knowledgeable on data warehousing implementation

·        Should be proficient in data modeling for both operational systems and analytical systems

·        Must be able to communicate effectively

·        Should possess the ability to work independently with minimal supervision or with a team depending on the scenario.

• Knowledge on SDLC with emphasis on design phase or any other Agile methodologies

·        Excellent communication

·        Excellent leadership

·        Collaboration

Hard SKills

·        Data Modeling

·        Database Technologies

·        Data Warehousing

·        Data Integration

·        Cloud Technologies

COMPETENCIES

·        Data Design: Ability to create and review data models and mappings

·        Stakeholder Engagement: Collaborative skills to engage with business stakeholders.

·        Process Improvement: Expertise in optimizing data architecture processes and workflows.

·        Data modeling: The ability to design and create a conceptual, logical, and physical representation of data. These skills are crucial for organizing data effectively within a database. a data warehouse or a data lake, ensuring it's accurate, consistent, and easily accessible.

·        Data mapping: A core part of data management that involve the ability to define relationships between data fields from different sources. This is crucial for ensuring that data remains accurate, consistent, and usable during processes like data migration, data integration, and data warehousing.

·        Stakeholder Engagement: Collaborate with business stakeholders to understand their data requirements and ensure data solutions meet their needs.

·        Process Improvement: Continuously improve data architecture experience and processes, implement sound practices, and optimize workflows for efficiency.

·        Documentation Oversight: Ensure the maintenance of documentation, data dictionaries, metadata  and data lineage within the team.

·        Talent Development: Foster the professional growth of team members through mentoring, training, and skill development.

KPIs:

·        Data Model Completeness: Measure the completeness of a design against the requirements.

·        Design Review Success Rate: Assess efficient resource allocation

·        Stakeholder Satisfaction: Evaluate stakeholder feedback on data solutions.

·        Process Efficiency: Track improvements in data architecture processes.

·        Budget Adherence: Ensure data-related projects stay within budget.

Data Mapping accuracy rate: Measure the correctness of the logic implemented in the data design

·        Adherence to Data Governance Policies: The degree to which data models and processes comply with data governance policies, which can be measured by the number of policy exceptions or compliance violations.

TOP 3-5 DELIVERABLES:

·        Design Accuracy, Efficiency, and Compliance: Correct and optimized design that supports business objectives and adheres to project budget while ensuring compliance with governance policies.

·        Project Execution: Successful completion of data architecture projects within timelines and budgets.

·        Stakeholder Alignment: Data solutions that meet business requirements.

·        Process Optimization: Streamlined data architecture processes and workflows.

Budget Oversight: Effective budget management for data-related initiatives