Data Engineer with 1+ years in SQL & Python for Financial Data Operations
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Data Analyst and aspiring Data Engineer with hands-on experience at Morningstar India, specializing in SQL, Snowflake, Python, and PySpark for large-scale financial data operations. Built and shipped COHO a full-stack internal data platform used by the Private Markets team to automate LP fund filing ingestion, AI-powered entity extraction, and fund reconciliation at scale. Experienced across the full data lifecycle — pipeline architecture, ETL design, validation, and delivery — with a strong foundation in private markets data, cloud infrastructure (Snowflake, Azure), and production-grade AI tooling.
Osmania University
B.E. · Electronics and Communication Engineering
N/A – June 30, 2024
Morningstar India
Data Analyst
January 1, 2025 – Present
India
Labit Agro Pvt. Ltd.
Full-Stack Developer Intern
September 1, 2024 – December 1, 2024
India
Echo-Vision: Auditory Guidance System for the Visually Impaired
June 18, 2026 – Present
Developed a real-time embedded assistive system using computer vision models, ultrasonic sensors, and text-to-speech to provide audio-based navigation guidance; achieved 90%+ obstacle detection accuracy through iterative real-world testing. Awarded Best Final Year Project (2024).
COHO - Internal AI Data Platform for Private Markets (Morningstar)
June 18, 2026 – Present
Designed and delivered a full-stack internal data platform adopted by Morningstar's Private Markets Secondary team to replace manual LP filing workflows — covering the complete pipeline from document ingestion to validated, analyst-ready structured output. Core platform architecture: batch document queue manager → OCR extraction layer → LLM-based entity parser (Groq llama-3, adaptive 8b/70b model routing by complexity) → fuzzy-match fund resolver against PitchBook reference data → editable validation interface → 22-column CSV export per filing. Implemented intelligent LLM token and capacity management for a shared team API key, dynamically routing filings between fast and high-accuracy models to balance throughput and extraction quality at scale. Delivered as a self-contained portable application with embedded runtime, enabling zero-setup deployment for non-technical analysts — no installation, no configuration, no dependencies. Impact: ~70% reduction in manual processing time per filing cycle; full adoption across the analyst team within weeks of launch.
Microsoft Azure Data Fundamentals (AZ-900)
Microsoft
June 1, 2026 – Present
AWS Certified Cloud Practitioner (In Progress)
AWS
June 1, 2026 – Present
Google Cloud Skill Badge – BigQuery for Data Analysts
June 1, 2026 – Present
Financial Markets
Yale University (Coursera)
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience with both academic (Echo-Vision) and professional (COHO) projects, coupled with their pursuit of multiple cloud certifications, demonstrates a diverse skill set and a proactive learning attitude. Their involvement in organizing technical events suggests a collaborative and community-oriented mindset. The COHO project's impact on reducing manual processing time by ~70% and achieving full adoption indicates a results-driven and impactful approach, aligning well with a performance-oriented culture. The breadth of technologies and domains (AI, finance, embedded systems, full-stack web) suggests adaptability and a willingness to tackle varied challenges.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, evidenced by the complex COHO platform development and the Echo-Vision project. Their ability to package a technical solution for non-technical users (COHO) indicates good user empathy and a practical approach to deployment. The description of leading a team for technical events suggests leadership potential and collaboration skills. The candidate's proactive pursuit of multiple cloud certifications indicates a strong drive for continuous learning and adaptability.