Solution Architect

The Technical Architect is the visionary responsible for the system's design. They determine how the data quality framework will be built and what technologies will power it.

• System Design: Design the end-to-end data architecture, ensuring the DQ framework is scalable, secure, and fits within the broader enterprise data ecosystem.
• Tool Selection: Evaluate and select the right data quality, profiling, and cataloging tool where applicable

• →Framework Definition: Define the core DQ dimensions to be measured (Completeness, Uniqueness, Timeliness, Validity, Accuracy, Consistency) and establish the standardized rules for data governance.
• →Best Practices: Set coding standards, deployment strategies (CI/CD for data), and devise the error-handling mechanisms
• →Engineering Standards: Set Python and SQL coding standards, branching strategy, CI/CD for data (build, test, promote), automated quality gates, observability and error-handling mechanisms

Technical Governance: Conduct design and code reviews, validate non-functional requirements (performance, volume, availability) and guide the engineering team

·       Solution Architecture Document (SAD): The master blueprint (often including architectural diagrams) detailing how data flows from source to target, and where DQ checks are applied.

·       Tool Evaluation & Recommendation Matrix: A formal comparison of evaluated DQ tools detailing why specific technologies were chosen.

·       Data Quality Framework Blueprint: The documented standards for how DQ will be implemented system-wide, including error-handling logic and CI/CD deployment strategies.