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Data Analyst with less than a year in Python, SQL & Power BI, skilled in building data pipelines & i
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Assessing your cultural and operational fit
Detail-oriented Data Analyst with hands-on experience in Python, SQL, Power BI, and modern AI techniques including RAG systems. Skilled in building end-to-end data pipelines, interactive dashboards, and intelligent applications. Google Data Analytics certified with strong expertise in data cleaning, analysis, visualization, and deriving actionable business insights.
TERI PG College, Ghazipur
Master of Computer Applications (MCA)
August 1, 2024 – June 30, 2026
TERI PG College, Ghazipur
Bachelor of Computer Applications (BCA)
August 1, 2021 – June 30, 2024
Loan Dataset Analysis and Risk Dashboard
January 1, 2026 – June 1, 2026
- Built an automated end-to-end analytics solution by integrating MS SQL Server with Power BI using Dataflows and auto-scheduling for real-time data refresh. - Performed rigorous data validation and cleaning using Excel Pivot Tables to ensure high data quality before loading into Power BI. - Developed advanced DAX measures, calculated columns, and KPIs to create dynamic dashboards for Loan Overview, Demographics, and Risk Metrics. - Key Insights: Unemployed applicants showed the highest default rate (3.39%); applicants with lower credit scores took higher median loan amounts. - Delivered automated, insight-rich dashboards that support faster credit decision-making and improved portfolio risk monitoring.
RAG-Based AI Teaching Assistant for Data Science Course
January 1, 2026 – June 1, 2026
- Built an intelligent AI Teaching Assistant using Retrieval-Augmented Generation (RAG) to provide accurate answers from Data Science course video lectures. - Developed a complete data pipeline: Converted video lectures to MP3 audio, transcribed using OpenAI Whisper, performed text chunking, and loaded data into Pandas. - Designed and implemented end-to-end data ingestion and preprocessing pipelines to prepare content for embedding and semantic retrieval. - Enabled context-aware and accurate question-answering over course materials, significantly improving self-learning support for students.
Blinkit Quick Commerce Analysis (Google Data Analytics Capstone)
January 1, 2026 – June 1, 2026
- Analyzed large-scale order and delivery datasets using Google's structured framework (Ask, Prepare, Process, Analyze, Share, Act) to solve real business problems for a quick commerce platform. - Cleaned and transformed datasets containing order details, delivery times, and customer information using SQL and Python (Pandas). - Performed Exploratory Data Analysis (EDA) to identify peak ordering hours, regional performance, and factors affecting delivery time. - Built interactive dashboards in Power BI and Tableau to visualize order trends, delivery metrics, and customer behavior patterns. - Delivered actionable recommendations to optimize delivery operations and improve customer experience during high-demand periods.
Data Analytics Job Simulation
Deloitte Australia - Forage
June 1, 2026 – Present
Google Data Analytics Professional Certificate
Coursera
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong alignment with data analysis roles, covering various aspects from e-commerce to finance and AI. The diversity in project types (e.g., quick commerce, loan risk, AI teaching assistant) indicates a broad interest in applying data skills across different domains. The certifications further reinforce a commitment to continuous learning and professional development, which are positive indicators for cultural fit in a growth-oriented environment. However, the lack of professional experience means cultural fit in a corporate setting is yet to be fully proven.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a detail-oriented approach and problem-solving skills. The 'Blinkit Quick Commerce Analysis' project demonstrates the ability to deliver actionable recommendations, indicating a business-oriented mindset. The 'Loan Dataset Analysis' project shows an understanding of data quality and risk monitoring, which are important for operational fit in data-driven environments. The RAG-based AI project suggests an innovative and self-learning attitude.