Аналитик данных

ZiMAD is a US mobile game publisher and developer that creates free-to-play projects, partners with internationally renowned intellectual properties, and focuses on delivering remarkable user experiences and sharpening mental well-being.

• Design and maintain the analytical transformation layer including staging, core, and mart models that are modular, incremental, tested, documented, and version-controlled;
• Establish engineering standards for analytics code covering transformation tooling, testing, code review, and CI;
• Own metric definitions across sources to reconcile granularity, attribution windows, and naming for ROAS, cohort LTV, retention, ARPDAU, and spend;
• Build the analytical datasets behind dashboards and reports on UA, monetization, creative performance, and incrementality;
• Make data self-serve by modeling and documenting datasets so product, UA, and monetization teams can answer their own questions;
• Own data quality for the models in scope through tests, freshness and consistency monitoring, and source-to-source reconciliation;
• Build the data access layer for AI agents including semantic descriptions, curated query interfaces, guardrails, and evaluation of agent output against trusted data;
• Optimize analytical workloads for query performance and cost.

• 3–5+ Years in analytics engineering, BI development, or data engineering, with production ownership of a transformation layer;
• Strong SQL and deep experience with window functions, arrays, materialized views, and AggregatingMergeTree;
• Hands-on production experience with a transformation framework like dbt, SQLMesh, or Dataform including modular models, testing, documentation, and CI;
• Solid analytical data modeling judgment for choosing between wide event or user-level tables and star schemas;
• Ability to turn ambiguous business questions into data models through conversation with stakeholders;
• Proficiency in Git, code review, and enough Python to automate your own work;
• Nice to have: experience with mobile games or apps and fluency in their core metrics like D7 ROAS, cohort LTV, ARPDAU, and retention curves; practical experience with AI/LLM tools in a data context such as text-to-SQL, RAG over structured data, or MCP agent integrations; orchestration tools like Airflow or Dagster; Superset or a comparable BI platform.

• Opportunity to work for a US company with a diverse portfolio of global free-to-play projects with a multi-million monthly user base;
• Involvement in the entire product development cycle;
• Career growth prospects within an international company;
• Flexible working schedule;
• Bonuses based on the achievement of KPIs and financial results of projects;
• Paid conferences, training including language courses, and workshops;
• Sessions with psychologists to improve mental health and well-being;
• Participation in charity projects.