What You'll Do
Product Analytics & Insights
- Build and maintain dashboards for key product metrics, including advertiser funnel, campaign journey, feature adoption, platform health, and inventory performance;
- Analyze user behavior, conversion funnels, cohorts, retention, and product trends to identify business opportunities and operational issues;
- Measure the impact of product initiatives through A/B testing, pre/post analysis, or other appropriate methodologies;
- Deliver actionable insights and recommendations to support product decisions.
Data Foundation & Analytics Enablement
- Define and standardize business metrics and analytical frameworks with Product teams;
- Develop and maintain analytics documentation, including metric definitions, data models, query templates, and best practices to enable self-service analytics and AI-assisted analysis;
- Partner with Analytics Engineers and Data Engineers on tracking, data models, schemas, and data pipeline improvements.
Collaborate With
- Product Managers to define metrics, prioritize business questions, and evaluate product performance;
- Analytics Engineers / Data Engineers to improve data quality, tracking, and infrastructure;
- Operations & Leadership to provide business visibility and support strategic decision-making.
What You'll Need
Must Have
- Strong SQL skills with extensive experience in CTEs, window functions, and large-scale data querying;
- Proficient in Python (Pandas, Jupyter) for data analysis, automation, and visualization;
- Hands-on experience in exploratory analysis, cohort analysis, funnel analysis, segmentation, and retention analytics;
- Experience measuring product impact using A/B testing or other experiment methodologies.
- Strong dashboard design and data visualization skills with a focus on business decision-making;
- Strong product mindset with the ability to translate business problems into analytical solutions;
- Excellent stakeholder management and communication skills, working closely with Product and Business teams.
Nice to Have
- Experience with Spark/PySpark, ClickHouse, BigQuery, Airflow, or dbt;
- Experience building metric dictionaries, semantic layers, or analytics knowledge bases;
- Familiarity with data modeling, tracking design, and collaboration with data platform teams;
- Experience in AdTech, Digital Advertising, E-commerce, Marketplace, or other data-intensive products;
- Knowledge of advanced experimentation, causal inference, or impact measurement techniques.
