01 Zakres zadań
- Define the enterprise AI data architecture vision, principles, and reference architectures.
- Lead cross-functional reviews with IT, security, legal/privacy, and business stakeholders to align on data readiness roadmaps.
- Establish data contracts for AI consumption (schemas, semantics, classifications, SLAs) and govern schema evolution for backward compatibility.
- Make the data catalog the system of record for lineage, ownership, definitions, and policy labels; integrate with intake/change management.
- Define standard data models and semantic conventions that improve joinability and reuse across domains.
- Implement an enterprise data quality framework and automated scorecards (freshness, completeness, accuracy, consistency).
- Monitor for anomalies and schema drift; publish AI data readiness dashboards (catalog coverage, lineage depth, PII detection coverage, contract adherence).
- Standardize patterns for ingestion, processing, storage, serving, and environment promotion using Airflow or other standard ETL/Orchestration tools and CI/CD for data workflows.
- Define secure, consistent access patterns/APIs for downstream analytics and AI consumers.
