Data Analysis & Querying (SQL)

This section demonstrates how I use SQL to extract, transform, and analyze data to uncover insights that support business decision-making.

Each case study focuses on solving real business problems by querying large datasets, identifying trends, and generating structured outputs that improve performance visibility, enable deeper analysis, and support strategic planning.

From revenue analysis and customer segmentation to operational reporting, these projects highlight my ability to turn raw data into meaningful, decision-ready insights.

Revenue Optimization & Demand Insights - Instacart SQL Analysis

Business Problem

Fragmented operational data limited visibility into delivery performance, inventory efficiency, and financial outcomes.

Approach

Built a centralized Power BI dashboard integrating operational, financial, and delivery data into a single reporting layer.

Key Insight

Delivery inefficiencies and poor stock management were driving performance gaps and increasing operational costs.

Impact

Enabled real-time performance tracking and improved decision-making across operations, inventory, and financial management.

Business Context

Instacart operates in a highly competitive, demand-driven grocery delivery market where product availability, customer purchasing behavior, and category performance directly impact revenue and operational efficiency.

However, fragmented sales data limited visibility into:

high-performing product categories
demand fluctuations across time (e.g., weekends vs weekdays)
underperforming or unsold inventory
profitability differences across product groups

This analysis was conducted to transform raw transactional data into structured insights that support smarter inventory decisions, revenue optimization, and improved customer experience.

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