Data Analyst

• Job Role : Data Analyst
• Location : USA (Remote)
• Long Term Contract

Job Description :

• 1. Relational Data Modeling
• Design realistic, normalized relational data models (dimension and fact tables) for four
• industry verticals: Financial, Healthcare, Manufacturing, and Retail.
• Ensure schema design supports representative industry use cases and natural-language
• querying patterns without exposing PHI/PII or sensitive data.
• Map data requirements to align with the Oracle AI Database Agent metadata indexing
• and query execution engine.
• 2. Synthetic Data Generation & Loading
• Develop and execute reusable Python/SQL scripts to programmatically synthesize data
• at scale.
• Generate target volume thresholds (~50–500 rows per dimension table;
• ~10,000–100,000 rows per fact table per industry).
• Perform one-time seed data loads into designated schemas within the shared Oracle
• Autonomous Database.
• 3. Query Validation & Golden Dataset Development
• Validate data quality, primary/foreign key integrity, and representative query coverage
• across all four schemas.
• Collaborate with the AI/Gemini engineering team to build prompt libraries, golden
• datasets, and sample Critical User Journeys (CUJs).
• Perform end-to-end testing (Question → SQL → Result Execution) to confirm the
• accuracy of generated SQL queries.

• Participate in cross-industry isolation testing to ensure dataset security across schema
• boundaries.
• 4. Post-Deployment & UAT Support
• Assist with User Acceptance Testing (UAT) bug fixing and schema refinements based on
• demo feedback.
• Track query performance and remediate gap accounts/schemas during the adoption
• push phase.
• Required Qualifications & Experience
• Data Analysis & Modeling: 3+ years of experience in data analysis, relational data
• modeling (star schema, snowflake, transactional), and data warehousing.
• Domain Knowledge: Understanding of schema structures across at least two target
• industries (Financial, Healthcare, Manufacturing, or Retail).
• Programming & SQL: Strong proficiency in SQL (Oracle dialect preferred) and Python
• (Pandas, Faker, or synthetic data generation frameworks).
• Data Synthesis & Quality: Experience generating synthetic/mock data for testing, demo
• environments, or AI model training while avoiding PHI/PII regulatory exposure.
• AI & Analytical Tools: Familiarity with AI/LLM concepts, prompt engineering baseline
• evaluation, or Natural-Language-to-SQL workflows is highly desirable.