Isha Apandkar

Data Analyst

Talent

Bellevue, KingMember since February 13, 2026

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About

Business Intelligence Engineer and Data Analyst with 5+ years of experience delivering scalable analytics and data-driven solutions across ecommerce, operations, and finance. Experienced in building production data pipelines, BI reporting systems, and applying machine learning techniques to solve business and operational problems.

Recently completed a Business Intelligence Engineering internship at Amazon, where I built SQL-based ETL pipelines, data models, and executive dashboards to support global marketplace expansion. I worked extensively with Amazon Redshift, complex SQL (CTEs, window functions), and BI tools such as Tableau and QuickSight to automate reporting and deliver actionable insights with measurable business impact.

Previously, I worked as a Business Analyst and Data Analyst at BNP Paribas, supporting large-scale operational and risk analytics systems. I managed high-volume data pipelines, improved data quality, automated validation processes, and created dashboards used by senior stakeholders across global teams.

In addition, I have hands-on experience applying machine learning techniques through projects involving predictive modeling, anomaly detection, recommendation systems, and classification problems. I have worked with Python-based ML libraries for data preprocessing, feature engineering, model training, evaluation, and performance optimization.

Core strengths:

Advanced SQL & data modeling (ETL, fact/dimension tables)
Tableau, QuickSight, Power BI dashboards
Machine Learning fundamentals (predictive modeling, feature engineering, model evaluation)
Ecommerce & operations analytics
Forecasting, KPI development, root-cause analysis
Stakeholder management & business requirements

Actively seeking full-time roles in:

Data Analyst | Business Intelligence Engineer | Analytics Engineer | Data Engineer

Tech stack: SQL, Amazon Redshift, Snowflake, Tableau, QuickSight, Power BI, Python (pandas, scikit-learn), AWS

Experience

Amazon, Seattle, WA, USA June 2025-Sept 2025

Business Intelligence Engineering Intern

Architected a consolidated ASIN lifecycle data mart and associated metrics to track global expansion of Amazon products, integrating signals from compliance and safety checks through marketplace availability and sales to enable end-to-end visibility into product health, weekly/monthly additions, and drop-offs.
Gathered requirements and built 8 scalable Redshift ETL pipelines and schemas using advanced SQL/CTEs (DDL/SDL) to power reliable ingestion, validation, and modeling.
Delivered 4 executive-facing QuickSight dashboards tracking ASIN additions and attritions at multiple metric levels (product, glance views, country, category), with deep-dive drilldowns to support investigations into drop-off reasons such as out-of-stock, not-buyable, and no-visibility.
Developed a GenAI agent (Vostok AI) to enable natural-language querying of dashboards and near real-time metrics, accelerating executive data discovery and ad-hoc analysis.
Improved operational efficiency and enabled ~$1M in quarterly business impact by surfacing revenue opportunities from ASIN drop-offs through automated dashboards, while saving ~40 hours per week by eliminating manual checks via self-serve root-cause analysis and monitoring.

BNP Paribas India Solutions Pvt. Ltd, Mumbai, Maharashtra, India Aug 2017 – March 2023

Data Analyst

Managed 100,000+ daily data feeds across global risk systems, ensuring 99.5% on-time delivery and reducing delayed reports by 30%.
Built automated validation and anomaly-detection checks that reduced manual review effort by 40% and lowered data errors by 25%.
Developed root-cause analysis KPIs for 200+ data-quality incidents per quarter, improving pipeline stability by 35%.
Decommissioned 20,000+ legacy jobs from Sybase ASE, simplifying system architecture and reducing maintenance workload by 20% across risk reporting workflows.
Created client centric Tableau dashboards for EU, APAC, and EMEA regions and presented quarterly risk trends and insights in senior leadership QBRs, supporting regional risk prioritization and planning.
Applied machine learning models (XGBoost) to predict portfolio-level risk exposure, enhancing early warning detection and supporting proactive risk mitigation.