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# Analytics Engineer

- **Company:** XIMAD Inc.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Query Performance, Artificial Intelligence, Airflow, Data Analysis, Business Intelligence Development, BigQuery, Code Review, Information Engineering, Data Mart, Python (Programming Language), PostgreSQL, Data Access Layer, SQL Databases, Workflow Management Systems, Large Language Models, Snowflake, Git, Star Schema, Vertica
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=486d49907b71fbd8

## About the Role

* 3-5+ years in analytics engineering, BI development or data engineering, with production ownership of a transformation layer
 * Strong SQL. ClickHouse experience preferred - window functions, arrays, materialized views, AggregatingMergeTree. Deep Postgres, BigQuery or Snowflake experience is welcome if you're keen to go deep on ClickHouse
 * Hands-on production experience with a transformation framework - dbt, SQLMesh, Dataform or equivalent - including modular models, testing, documentation and CI
 * Solid analytical data modeling judgment - knowing when a wide event or user-level table beats a star schema, and why
 * Ability to turn an ambiguous business question into a data model through conversation with stakeholders
 * Git, code review, and enough Python to automate your own work, * Experience with mobile games or apps and fluency in their core metrics - D7 ROAS, cohort LTV, ARPDAU, retention curves - and why numbers differ between sources
 * Practical experience with AI/LLM tools in a data context: text-to-SQL, RAG over structured data, MCP or similar agent integrations, evaluation of model outputs
 * Orchestration tools (Airflow, Dagster or similar)
 * Superset or a comparable BI platform

## Description

We build mobile games. User acquisition, monetization, LiveOps and product decisions all run on data - and this role owns the modeling layer everything else depends on: the data marts, metric definitions and semantic layer behind reporting, ad-hoc analysis and our AI-driven tools., * Design and maintain the analytical transformation layer - staging, core and mart models that are modular, incremental, tested, documented and version-controlled

 * Establish engineering standards for analytics code: transformation tooling, testing, code review and CI
 * Own metric definitions across sources. Reconcile granularity, attribution windows and naming so ROAS, cohort LTV, retention, ARPDAU and spend mean one thing company-wide
 * Build the analytical datasets behind dashboards and reports on UA, monetization, creative performance and incrementality
 * Make data self-serve: model and document datasets so product, UA and monetization teams can answer their own questions
 * Own data quality for the models in scope - tests, freshness and consistency monitoring, source-to-source reconciliation
 * Build the data access layer for AI agents: semantic descriptions, curated query interfaces, guardrails, and evaluation of agent output against trusted data
 * Optimize analytical workloads for query performance and cost


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