Oracle PL/SQL Consultant

The Data Architect is responsible for preparing the Strategic Process Inventory (SPI) data estate for use by the GenAI platform. This involves defining the data onboarding approach, identifying required source structures, designing performant access patterns, and ensuring metadata is complete, accurate, and usable by downstream semantic and AI context layers., * Identify and document required Oracle database schemas, tables, views, columns, keys, and relationships for GenAI.

• Define the onboarding scope for structured data, including source systems, subject areas, and key data products.
• Extract, validate, and organize technical metadata from Oracle using tools like Toad, SQL scripts, or database dictionary queries.
• Establish metadata standards for table descriptions, column definitions, data types, and data classifications.
• Collaborate with business and data SMEs to validate data accuracy for the enterprise process management domain.
• Design optimized database views or materialized views to improve performance for GenAI query execution.
• Provide canonical source-to-target mapping between Oracle data structures and business concepts.
• Validate data quality, completeness, referential consistency, and metadata accuracy.
• Identify sensitive data elements that require access controls or masking.

Experience: 8+ years in enterprise data architecture, database architecture, or data platform architecture. Strong experience with Oracle relational databases and enterprise data models is required, as is experience designing performant data access patterns and working with metadata onboarding, data catalogs, and semantic modeling.

Technical Skills: Advanced SQL, especially Oracle SQL, and a strong understanding of PL/SQL concepts are necessary. A working knowledge of Python is preferred. Familiarity with YAML/JSON formats is useful. Experience with Oracle Database, Toad (or equivalent), data catalog platforms, and Git is also required.

Preferred Qualifications

• Experience with enterprise process management systems (POP, ARIS).
• Exposure to GenAI platforms and LLM-based enterprise solutions.
• Knowledge of regulatory and compliance-driven data environments.
• Experience with Airflow, dbt, Spark, or enterprise data pipeline tooling.

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