Marcelo López Castro

Data Analyst | Data Engineer

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About Me

My name is Marcelo, a Data Analyst and a Data Engineer with a background in Agronomic Engineering and a strong passion for transforming data into actionable insights.Passionate about data-driven decisions, I helped reduce costs by 50% at Ideas en Verde in a key service area by optimizing data workflows with SQL and Power BI.My goal is to join an innovative IT team where I can continue to grow, contribute with my analytical skills, and drive data-informed business decisions.

Skills

SQL Server | Power BI | Python | Tableau | AWS | Excel

Featured Projects

🟨 Python | SQL | AWS
ORDERS – ETL Pipeline and Cloud Integration (2025)

Built a fully functional local ETL pipeline using Python and real CSV order data. Applied cleaning and validation logic, generated logs, and automated the output upload to AWS S3 for querying in Athena using SQL.

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🟥SQL | Python | Power BI
MEDORO 6 – OPTIMIZATION OF SETUP AND PRODUCTION TIMES (2025)

Final and validated version of the Medoro project. It solves setup time duplication issues, corrects timestamp offsets, and introduces improved KPIs with dynamic Power BI dashboards using offline Excel replicas.

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🌿Power BI | SQL | Excel
IDEAS EN VERDE – OPTIMIZATION STRATEGIES IN CORPORATE LANSCAPING (2025)

Analyzes 2025 performance using 20+ years of data. Highlights seasonal efficiency shifts, strong client retention, and a strategic revenue concentration, all visualized through geographic and operational KPIs.

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🌍Tableau | Excel | Data Storytelling
WORLD HAPPINESS REPORT 2025 – GLOBAL INSIGHTS DASHBOARD

Interactive Tableau dashboard exploring the drivers of happiness across 130+ countries. Includes GDP, health, freedom, generosity, and corruption correlations, with custom maps, trend lines, and visual storytelling.

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Certifications

Google Data Analytics | Coursera |issued 2025

Data Analytics - Teaching Assistant | HENRY issued 2025

Data Analytics | HENRY |issued 2024

EF SET Certificate | issued 2025

Thanks!

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Accenture Stock Price Analysis (2001–2021)

SQL Server | Python | Power BI

This project explores over two decades of Accenture's stock price performance using real historical data sourced from Kaggle. The goal was to uncover long-term trends, identify volatility patterns, and deliver valuable insights for investors and stakeholders through an interactive Power BI dashboard.

🧠 Key Insights📈 The company’s stock price has shown strong growth since the early 2000s, peaking at $344.43 in 2021.🌍 Volatility increased during global events like the 2008 financial crisis and COVID-19, yet Accenture quickly recovered.💹 Monthly percentage variation reveals fluctuations in unstable periods and stability during sustained growth.💼 Daily performance remains consistent, ideal for long-term investment strategies.🔄 Transaction volume has increased steadily, signaling rising investor interest over time.

🛠️ Tech StackSQL Server: Data storage and queryingPython (pandas, numpy, matplotlib, seaborn): Data cleaning, EDA, and transformationPower BI: Dashboard design and storytellingData source: Kaggle CSV datasets

📸 Dashboard Screenshots

Cover Page

Price Evolution & KPIs

Performance Metrics

Key Insights Explained

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Ideas en Verde – Optimization (2003–2024)

Excel | SQL Server | Python | Power BI

This project analyzes more than two decades of operational data for Ideas en Verde, an indoor gardening company serving corporate clients in Argentina. The focus was on cost efficiency, sustainability, and strategic growth—especially after the disruptions of the COVID-19 pandemic.

🧠 Key Insights🌱 In-House Production: Increased from 0% to 75% after 2021, reducing external purchases and improving margins.💡 Efficiency Gains: Lower plant replacement rates due to better plant care and production strategies.💰 Pricing Optimization: Smart pricing agreements led to a 50% increase in profitability.📍 Geographic Insights: Over 55% of clients are concentrated in Buenos Aires' Microcentro, highlighting commercial targeting success.🌐 Digital Transformation: Launch of a new website and blog led to +20% in new client acquisition.

🛠️ Tech StackSQL Server: Long-table format modeling, data consolidation, and transformationPython: EDA with pandas, matplotlibPower BI: Interactive dashboards with custom filters and dynamic KPIsData Source: Historical CSV datasets and internal operational files

📸 Dashboard Screenshots

General Cover Page

Plant Replacement Trends by Client (2003–2024)

Metric Correlations and Operational Performance

KPI Overview – Min/Max by Year

Heatmap and Client Distribution

Summary of Key Business Improvements

• GitHub

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World Happiness Report 2024 – Global Insights Dashboard

Tableau | Excel | Data Storytelling

This project explores the key drivers of happiness across more than 130 countries, using the official dataset from the World Happiness Report 2024. Built entirely in Tableau, it offers an interactive and visual journey through global well-being indicators.

🔍 Key Insights✅ Finland, Denmark, and Iceland top the happiness index with scores above 7.5.
✅ Strongest positive correlations: GDP per capita, healthy life expectancy, social support, and freedom of choice.
✅ Generosity shows a weaker but still positive link to happiness.
✅ Countries with lower perceived corruption consistently report higher life satisfaction.
✅ The top 20 happiest countries all scored above 6.8.🛠️ Tools & Methods🔹 Tableau dashboards with interactive filters, tooltips, and navigation menu.
🔹 Thematic maps, trend lines (2011–2024), scatter plots, and a ranked word cloud.
🔹 Data source: World Happiness Report 2024 (CSV & XLSX).
🔹 Focus on cross-country comparisons, correlation analysis, and user experience.📊 Visual Highlights🌍 Global Map of happiness scores
📈 Trends by Country (selectable by dropdown)
💰 Income vs. Happiness (log GDP & Life Ladder)
❤️ Health and Longevity Impact
💙 Generosity and its weak correlation
🧑🤝🧑 Social Support and emotional well-being
🚨 Perceived Corruption and happiness scores
🗽 Freedom of Choice and satisfaction
🌟 Top 20 Word Cloud with happiness categories
📌 Summary of Key Findings

🚀 Impact & ConclusionThis dashboard illustrates how data storytelling can uncover meaningful global patterns. Designed as a portfolio project, it leverages Tableau’s full interactivity to guide users through the complex relationships between economic, social, and emotional factors shaping well-being worldwide.

📸 Dashboard Screenshots

Navigation Menu

General Cover Page

Happiness World Map 2024

Happiness Trend by Country

GDP per Capita Contribution

GDP vs. Life Ladder

Life Expectancy & Happiness

Generosity and Happiness

Social Support Impact

Perceived Corruption

Freedom of Choice

Top 20 Happiest – Word Cloud

Key Insights

• GitHub

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Ideas en Verde – Optimization Strategies in Corporate Landscaping (2025)

Power BI | SQL | Excel

This project builds on previous analyses of Ideas en Verde (2003–2024), incorporating updated 2025 data and improved KPIs. It explores seasonal efficiency, production strategies, and client revenue contribution to optimize long-term operations in corporate landscaping services.

🔍 Key Insights:✅ Seasonal Efficiency Drop: In-house plant production decreases sharply between July and November due to winter conditions.
✅ Strategic Shift Post-Pandemic: Self-production rates surged from 2021, peaking at 75.4% in 2023.
✅ Client Retention: Most clients have long-term relationships exceeding 100 months.
✅ Revenue Concentration: 20% of clients contribute over 60% of total revenue, guiding commercial focus.🛠️ Tools & Methods:📊 Power BI dashboards with drill-through navigation, slicers, and custom visuals.
🧹 Data cleaning and modeling in SQL and Excel to unify 20+ annual tables (2003–2025).
🗺️ Interactive geolocation mapping of client revenue clusters.

📸 Dashboard Screenshots:

Cover Page

Client History Overview

Production Input Breakdown – Monthly vs. Historical

Own Production Value Over Time (2003–2024)

2024 Revenue Distribution by Client and Location

Summary of Findings and Strategic Takeaways

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🔁 ETL Pipeline with Athena Integration - AWS Orders Project (2025)

Python | AWS | SQL

This technical project demonstrates a fully functional local ETL pipeline built in Python. It performs data cleaning, validation, logging, and exports a clean CSV file to AWS S3, where it's queried using SQL via Amazon Athena. The process includes file structure automation, error tracking, and cloud upload with SQL schema creation for analytics.

🔍Key Insights:✅ Raw .csv orders file is transformed into a clean, validated dataset.
✅ A new column is created during transformation to track total amount per order.
✅ A validate.py script ensures schema consistency and highlights potential issues.
✅ Execution logs are generated for each pipeline run and stored separately.
✅ Final clean file is uploaded to AWS S3 and connected to Athena for SQL queries.
✅ Athena queries allow immediate aggregation and filtering of cloud-stored data.🛠 Tools & Methods:🔹 Python: modular scripts for transform, validate, and main, with logs.
🔹 AWS S3: data storage and access via marcelo-orders-bucket.
🔹 Athena: external table creation, SQL schema parsing, live queries.
🔹 Logging system: timestamps of each run stored in .log file.
🔹 Folder structure: organized into data/raw, data/output, logs, and scripts.

🖼️ Visual Highlights:

Process diagram of the full ETL flow (main.py + S3 + Athena)

Project Structure

Environment Setup

Raw Data (Excel View)

main.py – Orchestrating the Pipeline

Raw CSV Preview (VS Code)

Clean CSV with Total Amount Column

Execution Log – etl.log (VS Code)

AWS S3 Bucket – Uploaded Clean File

Athena Table – Table Creation SQL Script

Athena Query – Average Quantity by Product

• GitHub

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🛠️MEDORO 6 – Setup and Production Optimization

SQL | Python | Power BI

🔍 Key Insights✅ Corrected a critical 2-day timestamp offset in preparation records.
✅ Solved the historical issue of duplicated setup durations.
✅ Identified multiple valid setup events per day per order (previously ignored).
✅ Introduced a new efficiency KPI: % Setup Time with traffic-light segmentation.
✅ Fixed time calculation inconsistencies and restored actual production durations.🛠️ Methods & Tools🔹 SQL Server: CTEs, time sequence validation, ID normalization.
🔹 Power BI: offline dashboards with dynamic measures and visual indicators.
🔹 DAX: custom KPIs for preparation and production efficiency.
🔹 Excel integration for stakeholders without SQL Server access.

This is the final, validated version of the Medoro analysis for setup and production time optimization in a manufacturing context.

📸 Dashboard Screenshots

Cover Page

KPI Dashboard – All Orders

Order ID 14620 – Setup vs Production Timeline

Order ID 14626 – Setup vs Production Timeline

Process Table – All Orders

Order ID 14471 – Process Block Table

KPI Dashboard – All Orders

KPI Dashboard – Single Order 14620

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