Ejemplo CV: Data Analyst

Data Analyst

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Ejemplo de CV: Data Analyst

El CV Completo

ANDRÉS LÓPEZ

Bogotá, Colombia | +57 310 123-4567 | andres.lopez@email.com | linkedin.com/in/andreslopez-data | github.com/alopez-data

PROFESSIONAL SUMMARY

Data analyst with 3+ years extracting actionable insights from complex datasets using SQL, Python, and Tableau. Identified $1.2M revenue opportunity through cohort analysis and reduced customer churn by 18% via predictive modeling. Seeking remote data analyst role to drive data-informed business decisions.

WORK EXPERIENCE

Data Analyst | RetailCorp LATAM (Remote) | Aug 2022 - Present

Analyzed 5M+ customer transactions using SQL and Python to identify $1.2M upsell opportunity, driving 15% revenue increase
Built automated Tableau dashboards for C-level executives, reducing reporting time from 8 hours to 15 minutes weekly
Developed churn prediction model (Logistic Regression) achieving 82% accuracy, enabling retention campaigns that reduced churn by 18%
Collaborated with product team to A/B test 12+ features, improving user activation rate by 24%

Junior Data Analyst | Marketing Agency | Jan 2021 - Jul 2022

Cleaned and analyzed campaign data for 15+ clients, processing 2M+ records monthly in Excel and Google Sheets
Created 40+ visualizations in Tableau and Looker Studio, supporting $500K+ in ad spend optimization
Performed cohort analysis and RFM segmentation, increasing email campaign CTR by 35%
Presented monthly performance reports to clients, maintaining 95% satisfaction rate

Data Analytics Intern | Fintech Startup | Jun 2020 - Dec 2020

Extracted data from PostgreSQL database using SQL queries for product and growth teams
Built ETL pipeline with Python (Pandas) to automate data ingestion from 3 external APIs
Conducted exploratory data analysis (EDA) to identify trends in user behavior, informing product roadmap

TECHNICAL SKILLS

Languages & Tools: SQL (PostgreSQL, MySQL), Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), R (basic) Visualization: Tableau, Power BI, Looker Studio (Google Data Studio), Plotly Databases: PostgreSQL, MySQL, BigQuery, Snowflake Data Processing: Excel (Advanced), Google Sheets, ETL pipelines Statistics: Hypothesis Testing, A/B Testing, Regression Analysis, Predictive Modeling Other: Git, Jupyter Notebooks, Google Analytics, Segment, Mixpanel

EDUCATION

Bachelor of Science in Industrial Engineering | Universidad Nacional de Colombia | 2016 - 2020 GPA: 4.2/5.0 | Relevant coursework: Statistics, Operations Research, Data Mining

CERTIFICATIONS

Google Data Analytics Professional Certificate (2022)
Tableau Desktop Specialist (2023)
SQL for Data Science (Coursera - University of California, Davis) (2021)

PROJECTS

Customer Churn Analysis (GitHub)

Built end-to-end ML pipeline predicting churn with 82% accuracy using Python and Scikit-learn
Visualized findings in Tableau dashboard with actionable recommendations

Sales Forecasting Model

Time series forecasting using ARIMA and Prophet, achieving MAPE < 8%

LANGUAGES

Spanish: Native
English: Fluent (C1) - TOEFL iBT: 100/120

Anotaciones Clave para Data Analysts

1. Business Impact Antes que Técnica

❌ Enfoque técnico sin contexto:

- Escribí queries SQL complejas
- Creé visualizaciones en Tableau
- Usé machine learning para análisis

✅ Enfoque en impacto de negocio:

- Identified $1.2M revenue opportunity through SQL cohort analysis
- Reduced reporting time from 8 hours to 15 minutes with Tableau automation
- Reduced churn by 18% via predictive ML model

Fórmula: Técnica + Resultado de Negocio

Los stakeholders quieren saber: ¿Cómo ayudaste a la empresa a ganar más dinero o ahorrar costos?

2. Keywords de Herramientas Específicas

Los ATS buscan herramientas exactas:

Visualization:

Tableau ✓ (más demandado)
Power BI ✓
Looker / Looker Studio ✓

Databases:

PostgreSQL, MySQL ✓
BigQuery (Google Cloud) ✓
Snowflake ✓

Languages:

SQL (obligatorio) ✓
Python: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn ✓
R (bonus) ✓

Analytics Platforms:

Google Analytics ✓
Mixpanel, Amplitude, Segment ✓

Tip: Lee la job description y prioriza las herramientas que ellos mencionan.

3. Cuantifica Tamaño de Datasets

Demuestra escala:

Analyzed 5M+ transactions...
Processing 2M+ records monthly...
Built ETL for 3 external APIs...

Diferencia entre junior y senior:

Junior: "Analicé datos de clientes"
Mid: "Analyzed 2M+ customer records monthly"
Senior: "Built data pipeline processing 100M+ events daily"

4. Mezcla SQL + Python + Viz Tools

El stack clásico de data analyst:

SQL para extraer datos
Python/Excel para limpiar y analizar
Tableau/Power BI para visualizar
Storytelling para presentar insights

Ejemplo completo:

Extracted user behavior data (SQL) → Cleaned 2M records (Python Pandas) →
Built churn prediction model (Scikit-learn) → Visualized in Tableau dashboard →
Presented to C-suite, driving retention campaigns (-18% churn)

5. A/B Testing y Experiment Design

Keyword mágico para product analytics:

Collaborated with product team to A/B test 12+ features

Demuestra:

Conocimiento de estadística (hypothesis testing, p-values, confidence intervals)
Pensamiento científico (control vs. treatment)
Colaboración cross-functional (product, growth, marketing)

Herramientas mencionables:

Optimizely, VWO (A/B testing platforms)
Google Optimize
Statsig, Eppo

6. Dashboards Automatizados

Frase clave:

Built automated dashboards reducing reporting time from X to Y

Valor para employer:

Reduces manual work → ahorra tiempo del equipo
Self-service analytics → stakeholders toman decisiones más rápido
Real-time data → insights más oportunos

Menciona herramientas de automatización:

Airflow (ETL orchestration)
dbt (data transformation)
Google Apps Script, Python scripts

7. Certificaciones Relevantes

Para data analysts, certificaciones importan:

Top certifications:

Google Data Analytics Professional Certificate (Coursera) - entry-level
Tableau Desktop Specialist / Certified Associate - visualization
Microsoft Certified: Data Analyst Associate (Power BI) - BI tools
SQL certifications (cualquier proveedor)

Bonus:

AWS Certified Data Analytics
Google Cloud Professional Data Engineer

8. Projects en GitHub

Incluye 2-3 proyectos técnicos:

Proyecto típico:

Customer Churn Analysis (GitHub)
- Dataset: Telecom company with 10K customers
- Techniques: EDA, feature engineering, logistic regression, random forest
- Results: 82% accuracy, deployed Streamlit app for predictions

Formatos populares:

Jupyter Notebooks con análisis completo
Streamlit/Dash apps interactivas
Kaggle competitions (Top 10% en alguna)

Soft Skills Implícitos

No escribas "Communication skills" genéricamente. Demuéstralo:

❌ Vago:

- Excelentes habilidades de comunicación

✅ Demostrado con ejemplos:

- Presented monthly reports to C-suite executives
- Collaborated with product team on A/B test design
- Maintained 95% client satisfaction rate

Checklist Específico para Data Analysts

Tip final: Data analyst está entre tech y business. Tu CV debe mostrar habilidad técnica (SQL, Python) Y pensamiento de negocio (revenue, churn, conversion). No seas solo "query writer", sé "business partner con superpoderes de datos".