Resume Example: Data Analyst

Data Analyst

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Resume Example: Data Analyst

Complete Resume

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

Key Annotations for Data Analysts

1. Business Impact Before Technique

Formula: Technique + Business Result

Example:

Identified $1.2M revenue opportunity through SQL cohort analysis
Reduced churn by 18% via predictive ML model

Stakeholders want to know: How did you help the company make more money or save costs?

2. Specific Tool Keywords

Visualization:

Tableau ✓ (most demanded)
Power BI ✓
Looker / Looker Studio ✓

Databases:

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

Languages:

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

Analytics Platforms:

Google Analytics ✓
Mixpanel, Amplitude, Segment ✓

3. Quantify Dataset Size

Show scale:

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

Difference between levels:

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

4. Mix SQL + Python + Viz Tools

Classic data analyst stack:

SQL to extract data
Python/Excel to clean and analyze
Tableau/Power BI to visualize
Storytelling to present insights

Complete example:

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 and Experiment Design

Magic keyword for product analytics:

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

Demonstrates:

Statistical knowledge (hypothesis testing, p-values, confidence intervals)
Scientific thinking (control vs. treatment)
Cross-functional collaboration

6. Automated Dashboards

Key phrase:

Built automated dashboards reducing reporting time from X to Y

Value for employer:

Reduces manual work → saves team time
Self-service analytics → stakeholders make faster decisions
Real-time data → more timely insights

7. Relevant Certifications

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 (any provider)

8. GitHub Projects

Include 2-3 technical projects:

Typical project:

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

Data Analyst-Specific Checklist

Final tip: Data analyst is between tech and business. Your resume should show technical skill (SQL, Python) AND business thinking (revenue, churn, conversion). Don't be just "query writer", be "business partner with data superpowers".