Analytics Engineer

About the role

About the role We are looking for a proactive and forward-thinking Analytics Engineer to design, build, and scale our core data models, semantic layer, and AI-ready decision infrastructure across Amanotes.

In this role, you will bridge the gap between raw data pipelines built by Data Engineering and the analytical and operational needs of Product Managers, LiveOps, User Acquisition (UA), and Leadership. You will be responsible for transforming complex, high-volume player behavioral telemetry and business data into clean, well-documented, tested, and standardized dimensional models.

Beyond classical analytics engineering, you will pioneer the development of our "insight stack" — mapping data models and business questions into Model Context Protocol (MCP) tools and AI agent workflows, turning raw telemetry into reliable, automated, and self-serve business insights.

What you will do 1. Data Modeling & Semantic Layer Architecture & Maintenance

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Design, build, and maintain production-grade dimensional data models in Google BigQuery using dbt, standardizing core metrics (UA, ad monetization, IAP, player engagement, and retention) into a trusted single source of truth.

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Own and evolve the enterprise semantic layer across BI platforms (e.g., Metabase) and AI agent endpoints, preventing metric drift through clear versioning, deprecation policies, and canonical definitions.

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Maintain model health, cost, and query performance: optimize BigQuery execution (partitioning, clustering, incremental builds) while ensuring data freshness SLAs and anomaly monitoring.

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Enforce software engineering rigor across analytics: modular dbt development, automated CI/CD testing, Git version control, and peer code reviews.

2. Insight Stack & AI-Powered Decision Infrastructure

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Collaborate with data leadership to design and evolve the "insight stack," connecting structured data assets to AI agents and Model Context Protocol (MCP) tools.

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Translate recurring stakeholder decision flows (across Product, LiveOps, UA, and Management) into standardized inputs, queries, validation rules, and structured outputs for automated agent execution.

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Build tool schemas, prompts, and execution flows enabling AI agents to query verified datasets, run comparative checks, and draft decision-ready summaries.

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Establish guardrails, data validation, and permission boundaries ensuring AI agents operate safely without unmonitored raw database access.

3. Data Quality, Testing & Verification

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Establish comprehensive automated testing frameworks (dbt tests, schema validation, data integrity rules) to safeguard data reliability and timeliness across all core data marts.

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Define evaluation criteria and test suites to benchmark AI agent outputs against ground-truth queries, dashboards, and analyst outputs.