Product Data Analyst, Mobile Apps

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GoFastiMobile app analytics

Based in Brazil, available to work remotely., at least 4 hours of overlap with US Eastern business hoursContractJunior

Salary2,500 - 3,000 USD per month

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Job Details

Languages: Fluent English
Experience: 2+ years of experience in product analytics, data analytics, or a related role
Required Skills: PythonSQLGCPProduct AnalyticsData visualizationA/B testingBigQuerydbt

Have 2+ years of experience in product analytics, data analytics, or a related role.
Bring strong SQL skills, including CTEs, window functions, complex joins, and query optimization on large datasets.
Have hands-on experience with BigQuery and Google Cloud Platform.
Have working familiarity with dbt and modern data modeling practices.
Be skilled in funnels, cohorts, retention, and event data analysis.
Have experience with A/B testing, including hypotheses, sample size, statistical significance, and interpreting results.
Have programming ability, ideally in Python, for analysis and automation beyond SQL.
Use AI tools daily in your analytics workflow.
Have experience with a BI or visualization tool such as Datastudio, QuickSight, or similar.
Be fluent in English.
Have at least 4 hours of overlap with US Eastern business hours.
Mobile app company experience is a plus.
Experience with ad-monetized or subscription consumer apps is a plus.
Experience with AI-powered BI tools or agent-driven reporting workflows is a plus.

Own analysis for assigned mobile apps, covering activation, feature adoption, onboarding drop-off, engagement, and retention.
Translate product questions into well-scoped analyses and clear recommendations.
Build funnel, cohort, and retention analyses on large event datasets in BigQuery.
Partner with product managers and engineers on event tracking plans and instrumentation QA.
Run A/B tests from analysis through readout.
Develop and maintain SQL and dbt models for reporting and analysis.
Use AI tools to write and review queries, automate recurring reports, and speed up insight generation.
Build dashboards and self-serve reports for product owners.
Identify anomalies and data integrity gaps, then fix or escalate them.
Present findings to non-technical audiences and proactively surface trends and opportunities.

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