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Carolina Bernachea

Turning data into business decisions

SQL Server Analyst | Application Support | Data Analysis

📄 View Resume 💼 LinkedIn

Application Support & SQL Server Analyst with experience working on business-critical systems and data analysis.

Argentinian professional with experience working across LATAM, open to remote opportunities.

REAL EXPERIENCE

Incident Analysis — Oracle & Jira

📌 Problem

High number of failed transactions with no clear root cause, impacting business operations and response times.

⚙️ Solution

Analyzed database logs and system behavior, identified patterns in failures, and documented troubleshooting procedures.

📊 Impact

Reduced incident resolution time and improved visibility for support teams and internal stakeholders.

🧠 What I did:

Data Validation & SQL Troubleshooting — Retail Systems (Confidential)

📌 Problem

Inconsistent data and discrepancies between system outputs and expected results, affecting reporting accuracy.

⚙️ Solution

Performed SQL-based analysis to validate data, identify inconsistencies, and trace issues back to their source within the system.

📊 Impact

Improved data reliability and reduced time spent investigating reporting issues.

🧠 What I did:

Operational Monitoring & Reporting — Jira Dashboards

📌 Problem

Lack of visibility into incident volume, status, and resolution performance across support teams.

⚙️ Solution

Built dashboards and reports to track incidents, monitor progress, and provide visibility into operational performance.

📊 Impact

Improved tracking of incidents and enabled better prioritization and response management.

🧠 What I did:

PROJECTS

Retail Sales Analysis — Mercado Pueyrredón

📌 Problem

The company lacks visibility on which products drive revenue.

⚙️ Approach

Designed a SQL data model and Power BI dashboard to analyze sales by category, product and time.

📊 Key Insight

Beverages generate the highest revenue and sales are concentrated in a small set of products.

Internal Business Dashboard

Key Insights:

  • Beverages generate the highest revenue
  • Sales are concentrated in a few key products
  • Sales show variation across different periods

💡 Business Impact:

  • Enabled identification of revenue-driving categories to support inventory decisions
  • Highlighted product concentration in a small subset of items (Pareto effect)
  • Provided visibility into sales trends to support demand planning
  • Reduced reliance on intuition by introducing data-driven insights

Static Dashboard Preview:

Interactive Dashboard

🧠 Technical Decisions:

  • Normalized data model to simulate real retail structure
  • Used joins across sales and products for multi-level analysis
  • Calculated revenue using transactional granularity

💻 Technical Assets:

📄 View SQL Script 📊 Download Power BI

Delivery Analytics — Geo & Sales Insights

📌 Problem

The business lacks visibility on geographic performance and revenue distribution.

⚙️ Approach

Built a SQL-based data model and Power BI dashboard to analyze orders, revenue and geolocation patterns.

📊 Key Insight

Montevideo shows the highest order density, while some regions generate higher revenue with fewer orders.

Delivery Performance Dashboard

Key Insights:

  • Revenue distribution varies significantly by city
  • Montevideo shows the highest order density
  • Some regions generate higher revenue with fewer orders
  • Geolocation enables better delivery optimization

💡 Business Impact:

  • Identified high-demand geographic zones to optimize delivery coverage
  • Revealed areas with high revenue but low order frequency (growth opportunities)
  • Supported strategic decisions for expansion and logistics optimization
  • Improved visibility into geographic performance and demand distribution

Static Map Preview:

Interactive Dashboard

🧠 Technical Decisions:

  • Normalized data model to simulate real retail structure
  • Used joins across sales and products for multi-level analysis
  • Calculated revenue using transactional granularity

💻 Technical Assets:

📄 View SQL Script 📊 Download Power BI

Customer Analytics — Segmentation & Revenue Insights

📌 Problem

The business lacks visibility on customer value and segmentation.

⚙️ Approach

Designed a SQL data model and Power BI dashboard to analyze customer behavior, revenue and segmentation.

📊 Key Insight

A small group of customers drives most revenue, while many customers fall into low-value or at-risk segments.

Customer Analytics Dashboard

Key Insights:

  • Revenue is concentrated in a small group of VIP customers
  • Several customers show low engagement and low spending
  • Customer segmentation enables targeted strategies
  • Geographic distribution reveals growth opportunities

💡 Business Impact:

  • Identified high-value (VIP) customers driving the majority of revenue
  • Detected low-engagement and at-risk customers for retention strategies
  • Enabled customer segmentation for targeted marketing actions
  • Provided a foundation for customer lifecycle and retention analysis

Static Dashboard Preview:

Interactive Dashboard

🧠 Technical Decisions:

  • Normalized data model to simulate real retail structure
  • Used joins across sales and products for multi-level analysis
  • Calculated revenue using transactional granularity

💻 Technical Assets:

📄 View SQL Script 📊 Download Power BI

📩 Open to Application Support & SQL Server opportunities

📧 Contact Me 💼 LinkedIn