Ashmita Pal

Ashmita Pal

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Ex-Financial Data Analyst @ Mimecast | Graduate Student @ Northeastern University
Massachusetts, United States

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Résumé

Jobs verified_user 0% verified
  • Mimecast
    Financial Data Analyst Co-op
    Mimecast
    Aug 2024 - Dec 2024 (5 months)
    Engineered Tableau dashboards for 20+ KPIs across 4 product lines, improving forecast accuracy by 15% and guiding $500M+ ARR strategies. Accelerated reporting by 40% by optimizing Snowflake marts with dbt and performance-tuned SQL models. Automated Jira KPI reporting using dbt macros and SQL, delivering daily executive insights with zero manual effort. Streamlined QA workflows via Power Query and dbt tests, saving 20+ audit hours/month and improving KPI accuracy by 25%. Built a Tableau funnel for 5K+ trial users to identify conversion gaps and strengthen ARR forecasting.
  • Northeastern University
    Data Analyst
    Northeastern University
    Oct 2023 - Aug 2024 (11 months)
    Automated Snowflake and AWS ETL (S3, Glue, Lambda, Athena, Redshift), cutting manual ingestion by 90% and streamlining 50+ tables. Optimized AWS Glue jobs with a 5-member team, improving runtime by 30% and scaling to 1M+ records/month. Performed A/B testing on new KPI logic using t-tests and chi-square (95% confidence) to validate consistency and accuracy across reports. Built QuickSight KPI dashboards for financial and ops visibility, accelerating reporting cycles. Validated 100K+ multi-source records in PostgreSQL and Alteryx, ensuring 99.5% data reliability for cross-functional teams.
  • CORIZO
    Data Analyst Intern
    CORIZO
    Jan 2023 - Jun 2023 (6 months)
    Led a 3-member team to build ARIMA and Prophet models forecasting weekly product demand with 92% accuracy, improving stock planning and reducing inventory costs by 15%. Automated variance reporting with VBA, Power Query, and SQL, reducing manual effort by 80%. Developed Tableau dashboards visualizing forecast vs. actuals, category trends, and fulfillment KPIs, supporting strategic resource planning. Partnered with supply chain and finance teams to operationalize forecasts, cutting excess inventory by 10%.
  • Vellore Institute of Technology
    Data & ML Assistant
    Vellore Institute of Technology
    Jan 2022 - Dec 2022 (1 year)
    Created a Snowflake validation pipeline with SQL rule-based checks and integrity tests, resolving 60+ issues across 10 financial datasets. Designed Tableau dashboards to track KPIs and reduce audit prep time by 35%. Automated Excel-based reporting workflows (Power Query, PivotTables, VBA) to deliver timely, error-free reports for management reviews. Developed and maintained KPIs to track data quality and reporting efficiency, collaborating with cross-functional teams to resolve discrepancies. Optimized PySpark pipelines on Hadoop, reducing processing time by 40% and increasing data availability for downstream analysis.
Education verified_user 0% verified
  • Northeastern University
    Master of Science
    Northeastern University
    Sep 2023 - Dec 2025 (2 years 4 months)
    Data Analytics Engineering
  • Vellore Institute of Technology
    Bachelor of Technology
    Vellore Institute of Technology
    Jul 2019 - Aug 2023 (4 years 2 months)
    Computer Science and Engineering
  • G
    Google Foundations - Data Analytics
  • T
    Tableau Desktop Specialist
Projects (professional or personal) verified_user 0% verified
  • B
    Brewery Data Warehouse: API-Based ETL and Data Quality Automation
    Sep 2025 - Oct 2025 (2 months)
    Engineered a data pipeline in DuckDB and Python using Bronze-Silver-Gold layers, integrating API-based ingestion from Open Brewery DB for 9K+ records with full governance and lineage tracking. Modeled Star, Snowflake, and Factless schemas using CTAS and hash joins, optimizing query performance and analytics traceability. Automated data-quality checks (nulls, reconciliation, orphan keys), achieving 100% referential integrity and reducing manual QA by 90%. Visualized brewery insights with Matplotlib and Plotly, validating end-to-end pipeline reliability and data accuracy.
  • D
    Diabetes Data - Analysis and Patient Readmission Prediction
    Oct 2023 - Dec 2023 (3 months)
    Investigated a 10-year dataset from 130 US hospitals, 70K+ patient records to identify trends in diabetes care and drivers of early readmission. Engineered features (demographics, comorbidities, lab values) and applied ML models (Random Forest, Gradient Boosting, Logistic Regression), achieving an F1 score of 0.72 (vs. 0.65 baseline) with Logistic Regression. Visualized cohort-level risk factors using Matplotlib, Seaborn, and Pandas, identifying 3 high-risk patient groups for targeted interventions. Delivered evidence-based recommendations to improve glycemic control and reduce 30-day readmission risk.