Build your analytics career, without paying for one.

Average Analytics Specialist is a free, open-source toolkit for landing and leveling up in analytics roles: example resumes for six real career stages, plus scenario-based practice across SQL, statistics, analytics, analytics engineering, data science, and GenAI & Claude for analytics. No login, no paywall, no SQL engine running behind the scenes — just a static site anyone can fork, extend, and contribute to.

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Resume samples

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Entry-level and senior examples across three tracks — see what scope and impact actually looks like at each stage.

Entry-Level Data Analyst

Entry-Level

Raul Maldonado — Data Analyst | SQL, Excel, Tableau, Python

SQL
Excel
Tableau
Data Cleaning
Ad Hoc Analysis

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Senior Data Analyst

Senior

Raúl Maldonado — Senior Data Analyst | SQL, Tableau, KPI Strategy, Team Leadership

SQL
Tableau
KPI Design
Stakeholder Management
Mentoring

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Entry-Level Data Scientist

Entry-Level

Rahul Maldonado — Data Scientist | Python, SQL, Machine Learning, A/B Testing

Python
SQL
scikit-learn
A/B Testing
Statistics

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Senior Data Scientist

Senior

Raoul Maldonado — Senior Data Scientist | Python, ML in Production, Experimentation, Mentoring

Python
Machine Learning
Experimentation Platforms
MLOps
Mentoring

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Entry-Level Analytics Engineer

Entry-Level

Raul A. Maldonado — Analytics Engineer | SQL, dbt, Snowflake, Data Modeling

SQL
dbt
Snowflake
Git
Data Testing

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Senior Analytics Engineer

Senior

R. Maldonado — Senior Analytics Engineer | dbt, Snowflake, Warehouse Architecture, Data Governance

SQL
dbt
Snowflake
Warehouse Architecture
Mentoring

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Practice topics

Flip through scenario-based flashcards, filter by difficulty, and track what you've mastered — all saved locally in your browser.

Analytics

Metrics definition, product sense, stakeholder communication, and business reasoning.

15 cards

Analytics Engineering

dbt modeling, data warehouse design, testing, and pipeline patterns.

15 cards

Data Science

Modeling, evaluation metrics, feature engineering, and the statistics-to-ML handoff.

18 cards

SQL

Query writing, window functions, joins, and performance scenarios.

15 cards

Statistics

Hypothesis testing, distributions, experiment design, and inference.

15 cards

GenAI & Claude for Analytics

MCP, semantic layers, evals, and guardrails for putting Claude and other LLMs on top of analytics data safely.

20 cards

Why this exists

Paid data platforms run real SQL engines and grading pipelines behind every question, which is powerful but expensive to host and maintain. This project takes a different trade-off: every resume and every flashcard answer is written ahead of time, so the whole site is static HTML/CSS/JS that deploys for free on GitHub Pages. You lose the "run arbitrary SQL and get graded" experience, but you gain something that costs nothing to run and that anyone can contribute a resume or a question to.