Senior AI Data Engineer

🔍 Key Responsibilities

📊 Dataset Design & Curation
• Design and build realistic toy/dummy datasets at varying complexity levels (simple, moderate, complex).
• Ensure datasets reflect real-world scenarios while remaining clean, reproducible, and well-structured (CSV format).


✏️ Prompt Engineering
• Write concise, natural-language prompts (<40 words) tailored for business analysts.
• Ensure prompts are grammatically precise and align with project style (non-technical, not overly directive, no raw metric requests).


📈 Golden Plot Creation
• Reproduce target visualizations with aesthetic and analytical fidelity.
• Write clean, reproducible Python code using libraries like pandas, matplotlib, seaborn, and plotly.
• Maintain high-quality code standards: well-commented, organized, and reproducible.


✅ Correctness Criteria & Documentation
• Define correctness criteria tailored to open-ended insight prompts.
• Ensure criteria are flexible, accurate, and avoid assuming fixed solutions.
• Provide supporting documentation with expected values and metrics (e.g., means, correlations, test results).


🕵️ Quality Assurance
• Rigorously check each example against quality rubrics (correctness, completeness, clarity, justification).
• Maintain a high standard of accuracy across data, prompts, and visualizations.

Required Qualifications

• 10+ years of professional experience in data engineering or applied data science.
• Expert-level proficiency in Python (data wrangling, visualization, statistical testing).
• Deep understanding of data modeling, prompt crafting, and reproducible workflows.
• Exceptional attention to detail and quality control.
• Proven success in high-impact technical environments.

Preferred Qualifications

• Experience with statistics, hypothesis testing, and storytelling with data.
• Background in producing training data, developer tools, or reproducible research.
• Strong writing skills for crafting precise prompts and correctness criteria.