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Popular generalist data role with broad required skills and metro location drives high competition.
Core analytics skills transfer across industries, though product/event-analytics focus favors consumer-tech backgrounds.
Multiple mandatory technical requirements (SQL, Python, BI, dbt, Airflow, statistics) create strict shortlisting filters.
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Design and maintain real-time self-service dashboards that surface key performance indicators like engagement, retention, and monetization for instant team action.
Architect and automate A/B testing platforms including metric contracts, sample-size calculations, and statistical validation of feature impacts.
Develop and manage modular ETL data pipelines ingesting billions of event logs using dbt and Airflow, ensuring data quality and readiness for analysis.
Hands-on analytics experience with expert-level SQL and Python (pandas) skills.
Proficiency in BI platforms for creating complex dashboards with advanced calculations and visualizations.
Strong foundation in statistical methods such as regression, survival analysis, and causal inference basics.
Experience building and automating ETL pipelines using dbt and Airflow under version control.
Experienced in end-to-end analytics including scoping, metric definition, pipeline implementation, and stakeholder communication within agile environments.
Demonstrates ability to lead and mentor junior analysts and promote best practices in SQL and BI design.
Skilled at deep behavioral analytics including cohort, funnel, and lifetime value analysis to quantify product impact on user lifecycle and revenue.