Junior or Mid Level Data Analyst

San José - Costa Rica

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

We’re looking for someone who likes turning messy high-volume data into clear answers. You’ll spend your time writing thoughtful queries building lightweight analysis tools and translating questions into metrics people can actually use. This is a hands-on role: you’ll be close to the data close to the decisions and responsible for getting the details right.

What you’ll do

Write fast correct SQL to explore large datasets validate assumptions and build repeatable analyses.
Use Python to clean data run analyses prototype models/heuristics and automate reports (pandas notebooks scripts).
Define metrics that make sense then pressure-test them (edge cases missing data weird timestamps broken joins).
Build and maintain “single source of truth” datasets: derived tables views documentation and checks.
Communicate results clearly—charts when needed plain English always—so non-technical teammates can act on them.
Partner with engineering/product to improve data quality instrumentation and the overall analytics workflow.

What we’re looking for

Strong SQL: you’re comfortable with window functions CTEs query plans and debugging surprising results.
Strong Python for analysis: you can go from raw data to a clean reproducible answer quickly.
You care about correctness and can explain your logic (including what you don’t know yet).
You’re curious systematic and not afraid to dig until the numbers make sense.
You can write clearly: your analysis should be understandable a month later by someone else (including you).

Nice to have

Experience with data warehouses/lakes (Trino/Presto BigQuery Snowflake Redshift etc.).
Stats intuition: experimentation bias/variance tradeoffs uncertainty backtesting.
Familiarity with dashboards/BI (Looker Tableau Superset Metabase) and version control.

How we work

We like simple tools strong fundamentals and rigorous thinking. We care more about your reasoning than any particular framework. You’ll have autonomy and teammates who will happily argue (politely) about definitions until the numbers are defensible.

Interview process (typical)

A short conversation about your experience and what you like working on
A practical SQL exercise (realistic data realistic ambiguity)
A Python analysis exercise (cleaning reasoning not trick questions)
A discussion of past projects and how you communicate results

What you’ll do

Write fast correct SQL to explore large datasets validate assumptions and build repeatable analyses.
Use Python to clean data run analyses prototype models/heuristics and automate reports (pandas notebooks scripts).
Define metrics that make sense then pressure-test them (edge cases missing data weird timestamps broken joins).
Build and maintain “single source of truth” datasets: derived tables views documentation and checks.
Communicate results clearly—charts when needed plain English always—so non-technical teammates can act on them.
Partner with engineering/product to improve data quality instrumentation and the overall analytics workflow.

What we’re looking for

Strong SQL: you’re comfortable with window functions CTEs query plans and debugging surprising results.
Strong Python for analysis: you can go from raw data to a clean reproducible answer quickly.
You care about correctness and can explain your logic (including what you don’t know yet).
You’re curious systematic and not afraid to dig until the numbers make sense.
You can write clearly: your analysis should be understandable a month later by someone else (including you).

Nice to have

Experience with data warehouses/lakes (Trino/Presto BigQuery Snowflake Redshift etc.).
Stats intuition: experimentation bias/variance tradeoffs uncertainty backtesting.
Familiarity with dashboards/BI (Looker Tableau Superset Metabase) and version control.

How we work

Interview process (typical)