Results-driven Data Analyst with experience in leveraging advanced SQL, Python, and machine learning to transform complex datasets into actionable insights. Expert in developing predictive models, optimizing ETL pipelines, and crafting interactive dashboards with Tableau and Power BI, consistently improving data accuracy, reducing processing times, and enhancing strategic decision-making.
Data Analyst
Tempe, Arizona, US
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Summary
Led the development and deployment of predictive models and advanced analytics solutions for financial risk assessment, optimizing data accuracy and informing strategic decisions.
Highlights
Developed and deployed predictive models for financial risk assessment using scikit-learn, regression, and classification algorithms, improving risk prediction accuracy by 35%.
Executed comprehensive exploratory data analysis (EDA) and feature engineering on large-scale financial datasets with Python, SQL, and R, identifying key risk factors and trends to inform strategic decision-making.
Boosted data accuracy by 65% and relevance by 42% by integrating five external financial data sources and implementing real-time KPI dashboards with SAS.
Processed and integrated over 1TB of structured and unstructured financial data using Azure Blob Storage and Azure Synapse Analytics, optimizing ETL pipelines and advanced analytics workflows.
Designed and implemented interactive Power BI dashboards for financial trends, credit risk, and fraud detection, reducing manual analysis time by over 15 hours per month and enabling business leaders to make informed financial decisions.
Data Analyst
Tempe, Arizona, US
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Summary
Revamped data reporting systems and optimized data processing pipelines, significantly enhancing report accuracy and operational efficiency for stakeholders.
Highlights
Revamped a scorecard reporting system by integrating real-time upstream data pipelines, increasing report accuracy by 20% and accelerating stakeholder decision-making.
Engineered a scalable data ingestion pipeline using Python (Pandas, NumPy) and SQL, automating data cleaning and transformation, improving data integrity by 25% and reducing manual intervention by 40%.
Developed and optimized complex DAX expressions in Power BI, enabling real-time KPI tracking and enhancing user insights by 30% through interactive dashboards.
Redesigned data models and indexing strategies in SQL Server, reducing dashboard load time by 50% and improving overall system responsiveness.
Automated job scheduling and query performance tuning with ETL frameworks and SQL stored procedures, cutting data processing time by 40% and ensuring seamless data refresh cycles.
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Master of Science
Information Technology
Grade: 9
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Bachelor of Technology
Computer Science
Grade: 8.56
MySQL, MS SQL, MongoDB, Snowflake, SparkSQL, Oracle.
Power BI, Tableau, Data Bricks, MS Excel, Google Analytics, Data Modeling, Data Mapping, Data Mining, Data Extraction, Data Transformation, Salesforce, Zendesk, Confluence, Postman, Query Optimization, ETL.
NumPy, Pandas, Matplotlib, Statistics, Agile, Hadoop, Hive, PySpark, Seaborn, Sci-kit Learn, JIRA, KPI Analysis, Automation Power Query, SSIS, SSRS, SAS, Informatica Power Center, Regression, Sklearn, SciPy, Hypothesis Testing, Neural Prophet.
AWS (Amazon Web Services), Microsoft Azure, Jenkins, Git, CI/CD, Snowflake.
Python, R, SQL, NoSQL.
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Summary
Developed and deployed an end-to-end churn prediction pipeline using data cleaning, EDA, feature engineering, and a Random Forest model (85% accuracy), integrated with a Flask API for real-time predictions. Delivered business impact by reducing churn by 10% in the pilot phase and enabling stakeholders to track insights through dynamic Tableau dashboards.