Data Analyst

📍LATAM - 100% Remote

🤝Contract - USD

🗣️Advanced English skills is required

We are looking for 3 Data Analysts, for 3 different teams: Product & Growth; Operations & Finance; and Experimentation & Digital Journey.

Role Summary

Seek versatile Data Analysts specialized in Data Quality. These roles ensure reliable, actionable insights across product analytics, financial reconciliation, and experiment measurement by embedding measurement quality into instrumentation, data modeling, testing, and reporting. The candidates will own end-to-end data integrity, governance, and cross-functional collaboration to drive data-driven decisions.

Key Responsibilities

• Data Modeling & Reconciliation (Cross-Functional): Build and maintain semantic data models linking operational sources (ticketing, retail, inventory, marketing) to finance systems and product metrics. Engineer reconciliation processes comparing operational datasets to gold sources (general ledger, ERP) and define acceptable variances with automated outlier alerts.
• Instrumentation QA & Data Quality Automation: Audit and validate analytics instrumentation (web/app) end-to-end: data layer → Tag Manager → GA4 (or Mixpanel/Amplitude) → data warehouse → BI. Validate event firing, privacy settings, and campaign tagging (UTMs, pixels). Develop automated data quality tests (freshness, completeness, uniqueness, reconciliation) using SQL (Snowflake, BigQuery, Redshift), Python, and frameworks like Great Expectations; schedule via Airflow/Snowflake Tasks and alert on failures.
• Dashboarding & Reporting: Create and maintain dashboards in Power BI, Looker, Sigma, or Tableau that reflect accurate metrics and QA indicators (pass/fail badges). Ensure traceability of metrics and readiness for executive and stakeholder reviews.
• Data Contracts, Governance & Documentation: Define data contracts for critical datasets (schema, nullability, validity thresholds) and implement checks to enforce contracts. Document data models, QA processes, SOPs, and runbooks; manage task/incident logs in Jira or ServiceNow; collaborate with product, engineering, marketing, finance, and operations in an Agile environment.
• Cross-Functional Collaboration & Training: Partner with product, marketing, finance, and IT to plan measurement, drive data quality improvements, and educate business users on data governance and best practices.
• Data Quality Assurance in Diverse Domains: Support quality across Product & Growth metrics, revenue attribution and tagging for finance, and experiment measurement pipelines; contribute to continuous improvement of data collection, storage, and processing.

Core Technical Skills

• SQL & Python: Proficient SQL for querying/testing data in cloud data warehouses (Snowflake, BigQuery, Redshift). Python for data validation scripts and ETL/test automation; familiarity with Pandas and testing frameworks (PyTest, Great Expectations).
• Analytics Tools: GA4 (custom events, dataLayer), Google Tag Manager (GTM) or similar for web/app tagging, and ad conversion pixels (Google Ads, Meta, TikTok, CM360). Experience with tag validation tools (GA Debugger, Tag Assistant).
• BI & Visualization: Experience building dashboards in Power BI, Sigma, Tableau or Looker. Ability to translate raw data into clear visual reports. Familiarity with DAX/Power Query (for Power BI) or SQL-based modeling (for Sigma).
• Programming: Proficiency SQL skills for large-scale reconciliations (Snowflake or similar). Comfortable with Python for data validation scripts and ETL jobs. Experience with libraries like Pandas for data processing and PyTest or Great Expectations for automated tests. Basic JavaScript skills for debugging tags via browser DevTools (Console/Network).
• Web & Analytics Instrumentation: Deep familiarity with GA4, Google Tag Manager (GTM) or similar, custom events, dataLayer, and campaign tagging; basic JavaScript for debugging and tag validation.
• Data Quality & Observability: Exposure to dbt for transformation/testing and data observability platforms (e.g., Monte Carlo) is a plus. Understanding CI/CD for data assets (Git, GitHub Actions, Jenkins).
• Collaboration & Process: Experience with Jira or ServiceNow for issue tracking and Confluence (or internal wiki) for documentation. Strong communication skills to work with technical and non-technical stakeholders.
• Scripting & QA: JavaScript and DevTools for debugging, with SQL/Python to automate data quality checks.