Overview
Required Skills
SQL (PostgreSQL / Redshift, including DDL and DML)
5/5
Looker (LookML, dashboards creation)
5/5
Python (working with datasets, automation)
5/5
Google Analytics, Google Tag Manager
5/5
Excel
5/5
Requirements
- MUST: B2+ level of English proficiency (main communication language in the company)
- Experience querying data warehouses (SQL), analyzing data, and arriving at insights using qualitative techniques
- Proven experience working with top-level management and the ability to explain complex analysis in simple words and insights
- Ability to execute cross-department projects, with a solid understanding of how things get done
- Bachelors / Masters degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Economics, etc.)
- 2+ years experience as a Data or Business Analyst (preferably at an e-commerce brand with a high volume of complex data)
- Experience with at least one of the visualization tools (e.g., Looker, PowerBI, Tableau) – links to the portfolio are welcome
Responsibilities
- Monitor Product Performance: Track and analyze the performance of key product features and A/B test results to ensure our offerings meet user expectations
- Deep Dive Analysis: Evaluate trends across various product segments and user journeys, examining data at granular levels to unearth insights
- Real-Time Issue Identification: Proactively detect anomalies and issues in product data as they arise, and support troubleshooting efforts for quick resolution
- Insightful Reporting: Create detailed reports and dynamic dashboards that translate complex data into actionable insights for the product team
- Strategic Project Execution: Lead targeted projects designed to improve product performance and optimize user engagement
- Optimization Opportunities: Identify and recommend opportunities to enhance key performance indicators, driving overall product value
- Data Infrastructure Enhancement: Design, develop, and continuously improve our product data and analytics infrastructure
- ETL and Data Pipeline Management: Build and monitor efficient ETL processes and data pipelines that integrate multiple product data sources
- Collaboration with Data Engineering: Work closely with data engineers to ensure seamless data workflows and robust system operations
- Performance Monitoring and Reporting: Regularly assess and report on product performance metrics, facilitating ongoing improvements
- Scalable Dashboard Implementation: Deploy scalable dashboard solutions to provide clear visibility into product metrics and trends
- Executive Support: Assist senior leadership with data analysis, predictive modeling, and reporting to inform strategic product decisions
- Innovative Opportunity Exploration: Investigate emerging trends and new data-driven opportunities that could lead to breakthroughs in product innovation
- Ad Hoc Analysis: Conduct exploratory analyses to identify key factors that can enhance product performance and user experience
- Commitment to Data Accuracy: Ensure all analyses are executed with a meticulous attention to detail, validating findings and considering edge cases
- Cross-Functional Collaboration: Partner with teams across product, technology, retention, and marketing to drive data-informed initiatives and support overall business growth
