Nayab Gauhar

Analytical, detail-oriented Advanced Data Analyst with deep domain fluency in financial capital markets and high-velocity data systems. Proven expertise in leveraging Python (Pandas/NumPy), interactive Business Intelligence (Power BI), and advanced SQL methodologies to engineer automated end-to-end ETL pipelines. Adept at orchestrating high-performance data architectures that securely ingest and transform daily data points across Nifty 500 stocks with zero record duplication.

Independent Data Analyst & Platform Developer at Self-employed (2026-07 – Present)

• High-Performance SQL Dashboard Hydration: Custom-engineered Dalal Street Analyser, an end-to-end stock market analytics platform where the entire multi-page Screeners and Strategy Automation engine computes complex queries to render dynamic outputs in under 1 second.
• Rapid Multi-Tier Dataset E(T)L Ingestion: Built automated data processing pipelines that execute complete daily data refreshes—ingesting comprehensive Nifty 500 technical indicators, macro fundamentals, quarterly reports, and yearly financial statements—processing over 200,000+ (2 Lakh+) data values within an 8–9 minute daily window with 0% duplication errors.
• Regex Data Cleansing Gating: Designed rigorous data validation gates utilizing Regular Expressions (Regex Constraints) to enforce strict pattern matching, string alphanumeric cleansing, and security schema rules across all core entry fields.
• Advanced Data Upsert Management: Implemented high-performance data integrity rules inside MySQL using INSERT IGNORE and ON DUPLICATE KEY UPDATE (Upsert) statements, guaranteeing 100% duplicate-free logs and seamless real-time ledger updates without pipeline interruptions.
• Containerized Environment Management: Layered analytical scripts across a 7-node microservices grid containerized inside Docker and Docker Compose, maintaining complete pipeline isolation across secure virtual network bridges.
• Relational Database Engineering: Developed a centralized 13-table financial database warehouse, cleanly partitioning tablespaces to handle live ticker metrics, active alpha screeners, historical indicators, and secure system states.
• Linux Systems Storage Optimization: Proactively monitored and managed system disk boundaries on a Linux server environment, allocating NVMe block partitions via growpart and resize2fs to ensure continuous data stream persistence and zero data dropouts.

Bachelor of Computer Applications in Computer Applications – Krupanidhi Group of Institutions