Job Description:
Key Responsibilities
- Business Term and Definition Engineering
- Review strategic process inventory SPI PDFs business documents data dictionaries and metadata extracts to identify business terms and domain concepts
- Define clear business definitions for terms used by SPI users
- Identify synonyms abbreviations alternate names and commonly used phrases for each business term
- Distinguish between similar or overloaded terms that may map to different database structures depending on business context
- Maintain terminology consistency across the semantic layer and knowledge base repository
Oracle Metadata Analysis
- Use Toad and Oracle metadata extracts to understand available schemas tables columns keys and relationships
- Analyze physical data structures and determine which tables and columns support each business term
- Identify candidate joins filters dimensions measures and identifiers needed to answer common SPI questions
- Work with the Data Architect to validate table relationships materialized views and performanceoptimized access paths
Term to Data Mapping
- Create mappings from business terms to Oracle tables columns views and materialized views
- Ensure mappings are precise enough to support naturallanguagetoSQL generation
- Identify gaps where business terms lack clear physical data mappings
Semantic Layer Buildout
- Support creation of the Talk to SPI semantic layer by organizing terms entities attributes metrics relationships and rules
- Document basic relationships between business entities and database entities
- Define earlystage business rules where required such as calculation logic filtering rules eligibility rules or default interpretation rules
- Collaborate with the AI Context Engineer to publish validated mappings into the Knowledge Base Repository
- Collaborate with the AI Platform Engineer to test whether published mappings are correctly consumed by the GenAI platform
Validation and Testing
- Test sample naturallanguage questions against expected table and column mappings
- Validate whether generated SQL uses the correct SPI structures
- Document mapping issues ambiguous terms missing metadata and required SME clarifications
- Support iterative improvements based on testing SME feedback and GenAI query results
Primary Skill Oracle SQL PLSQL
Secondary Skill Data modelling and architecture
Tertiary Skill Python
Required Qualifications
- 4 years in data engineering metadata engineering semantic layer development data analysis or business data mapping
- Handson experience working with Oracle database metadata
- Experience connecting business terminology to physical database structures
- Experience working with business documentation PDFs data dictionaries and technical metadata
- Exposure to naturallanguagetoSQL semantic modeling or AI context engineering is strongly preferred
Programming Query Languages
- Strong SQL especially Oracle SQL
- Intermediate Python for metadata parsing document processing mapping automation and validation
- Working knowledge of YAML JSON for structured semantic and mapping artifacts
- Basic Markdown for documentation
- Optional familiarity with regular expressions for text extraction and term matching
Systems Tools
- Oracle Database
- Toad for Oracle
- Python libraries for metadata and document processing
- Git or source control for managing mappings
- Excel or CSV for earlystage mapping inventories
- Knowledge base repository or semantic layer repository
Desired Qualifications
- Experience with enterprise process management systems POP ARISSDAR
- Exposure to GenAI platforms and LLMbased enterprise solutions
- Knowledge of regulatory and compliancedriven data environments
Experience with data catalog tools vector search graph database or RAG tooling
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