Highly accomplished Data Analyst with over 4 years of experience driving business intelligence, data analytics, and robust data pipeline development in fast-paced cloud environments. Expertly leverages Python, SQL, and advanced ETL processes to automate workflows, optimize data models, and deliver actionable insights through predictive analytics and Power BI dashboards. Proven ability to apply Large Language Models (LLMs) for enhanced sentiment analysis, consistently improving campaign performance and achieving significant marketing ROI.
Data Analyst
Jersey City, New Jersey, US
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Summary
Drove significant improvements in data processing, system reliability, and business insights by building advanced ETL pipelines, implementing CI/CD workflows, and applying LLM models.
Highlights
Engineered ETL pipelines with Python, Redshift, and Databricks, eliminating 6-hour reporting delays and improving data transformation time by 35% to enable real-time transaction insights via Snowflake.
Streamlined data operations by implementing CI/CD workflows, reducing deployment time from hours to minutes, increasing system reliability by 95%, and enhancing data security and governance.
Developed robust validation frameworks featuring automated schema checks and metadata tracking, which reduced data errors by 40% and significantly improved data governance across multiple teams.
Designed and deployed interactive Power BI dashboards using SQL and DAX, boosting data accessibility for business users by 45% and decreasing ad-hoc reporting requests by 50%.
Leveraged LLM models to analyze over 10,000 unstructured customer feedback responses, generating sentiment insights that enhanced campaign performance and increased marketing ROI by 30%.
Data Analyst
Dallas, TX, US
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Summary
Optimized financial planning, automated data processes, and enhanced executive decision-making through advanced analytics and cross-functional reporting governance.
Highlights
Optimized resource planning through custom SQL queries and financial models, identifying underutilized budgets and enhancing forecasting accuracy to achieve over $2M in annual savings.
Developed automated data pipelines from Google Analytics and Adobe Analytics using Python, reducing manual effort by 80% and establishing secure, governed channels for daily performance tracking.
Designed and implemented executive dashboards in Tableau and Power BI, providing real-time KPIs that accelerated financial and operational decision-making and improved leadership alignment.
Spearheaded cross-team reporting governance initiatives, standardizing metrics and templates to resolve communication gaps, which improved project delivery by 30% and significantly enhanced stakeholder trust.
Data Analyst
Hyderabad, Telangana, India
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Summary
Analyzed large-scale datasets and optimized data pipelines using SQL and Python, while enhancing data accessibility and reporting efficiency through Power BI.
Highlights
Utilized SQL and Python for in-depth analysis of large-scale datasets, optimizing data pipelines and reducing processing time by 20%.
Designed and maintained Power BI dashboards that improved data accessibility by 40% and streamlined reporting workflows by 35%, facilitating faster, data-driven decision-making across departments.
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Master's
Computer Science
Courses
Python
Statistics
Big Data Technologies
Data Visualization
Analysis of Algorithms
Machine Learning
Computer Networks
Cloud Computing
Software Engineering
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Bachelor's
Computer Science
Courses
Programming Languages (C, C++, Java, Python)
Database Systems
Statistical Methods
Data Structures
SQL
Computer Networks
Operating Systems
Object Oriented Programming
Machine Learning
Data Mining
Awarded By
Department of Computer Science at Lamar University
Awarded for outstanding academic performance and significant contributions as a graduate student in Computer Science.
Awarded By
Charter Communications
Recognized for exceptional performance and impact during the initial period of employment at Charter Communications.
Published by
Journal of Composition Theory
Summary
Undergraduate research project published for its practical insights, subsequently recognized and cited by peers in academic presentations and departmental discussions.
Python, SQL, Scala, R, Java, JavaScript.
AWS Lambda, AWS EC2, AWS S3, Azure, GCP.
Apache Spark, Databricks, Snowflake, Apache Airflow.
MySQL, PostgreSQL, MongoDB, SQL Server, Oracle, Redis.
Power BI, Tableau, Looker, Matplotlib, Seaborn.
Advanced Excel, VBA, Power Query, Pivot Tables, XLOOKUP, Financial Modeling.
Scikit-learn, TensorFlow, Spark MLlib, XGBoost, Random Forest, Gradient Boosting, Natural Language Processing (NLP), Predictive Analytics.
GitHub, Docker, CI/CD Workflows.
ETL Pipelines, Data Modeling, Data Governance, Schema Checks, Metadata Tracking.
Business Intelligence, Data Analytics, Financial Modeling, Forecasting, KPI Tracking, Resource Planning, Risk Analytics, Real-time Monitoring, Problem Solving, Strategic Thinking, Decision Making.
Agile Methodology, Cross-functional Collaboration, Communication, Stakeholder Management.
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Summary
Developed a machine learning churn prediction model and applied natural language processing to customer feedback, integrating insights into dashboards for marketing teams.
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Summary
Built fraud detection models and created a real-time risk monitoring dashboard to identify suspicious patterns and prevent potential financial losses.