Product Analyst (Game)
We are looking for a Product Analyst to whom we can entrust the development of the product, the establishment of a data management system, the enhancement of processes, and the provision of business consulting on growth opportunities. By solving complex analytical tasks with us, you will influence the development of global hits in mobile game development.
Responsibilities
Behind every product is a strong analytics team, but interaction will not only be within it. Naturally, you will have to deal with the classic tasks of any game product analyst: analyzing the game economy, balance, player behavior, and much more. However, the most fundamental and important task for a Data Analyst in the team is to ensure decision-making processes that lead to sustainable long-term product development. Also, among the tasks are:
- Developing player segmentation for adapting the gameplay to the user experience
- Conducting analysis, preparing reports, and recommendations for changing games within the product team
- Organizing the collection and automated processing of all collected game data, developing dashboards for recurring reports
- Participating in the preparation and conduct of A/B testing
Requirements
- Excellent mathematical background — knowledge of probability theory and applied statistics at the level of a technical university graduate
- Experience in data analysis, product/marketing analytics
- Excellent knowledge and experience working with SQL
- Experience working with Python
- Business-oriented, ability to find practical solutions in limited time, not overcomplicate where it's unnecessary
- Proactive stance, ability and willingness to take responsibility for results, ability to act in situations of uncertainty
- Truly large scale: hundreds of millions of players, petabytes of data (we store everything from the last 7 years) and marketing limited by efficiency, not budget
- Data-driven approach – we measure everything, conduct experiments, and make decisions based on data wherever possible
- All conclusions are made with reliance on statistics, and analysts set the standards for decision-making. Even for employees far from analytics, confidence intervals are not an empty sound
- Opportunity to change processes and approaches to more efficient ones without lengthy approvals and bureaucracy
Tech stack
Modern and reusable in other companies stack (JupyterHub or Databricks + PySpark). Skills gained while working at Playrix remain relevant and easily monetizable.
- No restrictions on tools, resources are allocated quickly, and we have access to the entire AWS
- Data is collected through Appsflyer (marketing) and Amplitude (product)
- Raw data is stored in S3, ETL processes are managed by Airflow, and prepared tables and views are stored in Redshift. Data Quality, lineage, cataloging are managed through Monte Carlo
- Analysts manipulate data using SQL and Python in Jupyter/Databricks notebooks, and they prototype dashboards using Tableau
- We are developing self-service and analytical products for data consumers, relieving analysts of routine tasks
