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Data Analyst with less than a year in data pipeline validation and quantitative data analysis with e
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Assessing your cultural and operational fit
Computer Science graduate with a strong analytical foundation in Python, SQL, and PySpark, specializing in data pipeline validation and quantitative data analysis. Experienced in executing data quality checks, predictive modeling, and structured model evaluation techniques. Possesses deep curiosity in Generative AI frameworks, LLM performance monitoring, and model risk governance. Adept at translating complex algorithmic insights into clear, risk-focused summaries for cross-functional stakeholders.
Lovely Professional University Punjab
Computer Science and Engineering · Computer Science and Engineering
August 1, 2022 – June 30, 2026
City Convent School
12th with Science · Science
June 1, 2020 – May 31, 2021
City Convent School
10th with Science · Science
June 1, 2018 – May 31, 2019
Multi-Source Data Ingestion & Algorithmic Fusion Pipeline
March 1, 2026 – April 1, 2026
Developed an end-to-end data ingestion pipeline in Python to fuse 4+ fragmented datasets, establishing structured data validation frameworks for analysis. Executed multi-dimensional time-series and correlation analysis on data structures, identifying systemic trends and providing risk-focused summaries. Built scalable data pipelines handling 50,000+ rows of historical ACLED data using Pandas, reducing manual processing latency by 60%. Evaluated complex geospatial and temporal data components, translating quantitative algorithmic outputs into actionable insights for cross-functional partners.
View ProjectOperational Transaction Analytics & Data Integrity Framework
February 1, 2026 – March 1, 2026
Engineered data pipeline integrations using SQL Server and flat files to track key operational and risk metrics across 10,000+ transactions. Formulated complex analytical queries and statistical metrics to evaluate real-time dataset performance, cutting manual oversight effort by 50%. Implemented strict data quality checks and reconciliation logic to validate source data accuracy and test for underlying data integrity anomalies. Conducted structured analysis of business requirements to map out 8 critical analytical views, ensuring compliance with documentation standards.
View ProjectSAP Certified Data Analyst - SAP Analytics Cloud
SAP
March 1, 2026 – Present
Databases and SQL for Data Science with Python
Coursera
December 1, 2025 – Present
Data Visualization in Excel
Coursera
November 1, 2024 – Present
Data Analysis with Tableau
Coursera
November 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a focus on data analysis, pipeline development, and data integrity, which aligns well with a Data Analyst role. The breadth of skills listed (Python, SQL, PySpark, Pandas, NumPy, ML concepts, data visualization tools) and multiple certifications suggest a proactive and continuous learning mindset. However, the projects are academic and relatively short-term, limiting the assessment of long-term collaboration and adaptability in a professional team environment.
Soft Skills & Operational Fit
The candidate's project descriptions highlight an ability to translate complex algorithmic outputs into actionable insights for cross-functional partners, suggesting good communication and problem-solving skills. The focus on data quality checks and reconciliation logic indicates an attention to detail and operational rigor. However, without direct work experience, the operational fit and ability to handle real-world project complexities are yet to be fully validated.