Analytics Engineer

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

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.

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.

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

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

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

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

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

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

3. Data Quality, Testing & Verification

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

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

Build alerting and monitoring systems for data discrepancies, silent schema drift, and telemetry anomalies, driving swift incident resolution.

Document assumptions, data lineage, edge cases, and handoff criteria between automated agent analysis and human analyst deep-dives.

4. Data Governance, Catalog & Discoverability

Own and evolve the data catalog, managing metadata tags, descriptions, and ownership attributes across all modeled tables and views.

Curate and maintain the enterprise business glossary and canonical metric definitions, ensuring clear alignment between technical schemas and business context.

Maintain end-to-end data lineage from raw event telemetry to BI dashboards and AI agent endpoints to guarantee traceability and impact analysis.

Champion data governance policies, including access control tiers, data stewardship, and PII identification/masking.

5. Stakeholder Enablement & Collaboration

Partner closely with Data engineer to provide feedback on upstream data ingestion contracts, event tracking schemas, and warehouse performance optimization.

Empower Data Analysts, Product Owners, and Growth teams to self-serve trusted data through clean documentation, intuitive table design, and BI semantic layers (e.g., Metabase).

Act as an advocate for data literacy, reproducibility, and modern analytics engineering practices across the company.

Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related quantitative discipline (or equivalent practical experience).

3+ years of experience in Analytics Engineering, Data Engineering, or Advanced Data Analytics in a fast-paced environment.

Advanced proficiency in SQL and dimensional data modeling techniques (Kimball methodology, star/snowflake schemas, fact/dimension table design).

Hands-on experience with dbt (dbt Core or dbt Cloud) for data transformation, testing, documentation, and semantic layer modeling in production.

Proven track record of maintaining and governing a centralized semantic layer or metric store, including versioning, deprecation handling, and performance tuning.

Strong working knowledge of cloud data warehouses, preferably Google Cloud BigQuery.

Proficiency in Python for scripting, workflow automation, and integrating APIs/data services.

Experience with software engineering workflows: Git, version control, automated testing, and CI/CD pipelines.

Demonstrated experience with data governance, data catalogs, metadata management, and data lineage tools.

Fluency in English with strong written and verbal communication skills, capable of translating complex data architecture to non-technical stakeholders.

Nice to have

Experience in mobile gaming, adtech (mobile ad mediation, monetization, UA attribution), or consumer mobile applications at scale.

Familiarity with player telemetry tracking, in-game event design, or game analytics metrics.

Hands-on exposure to Model Context Protocol (MCP), LLM APIs, or AI agent frameworks for data analysis and querying.

Experience with workflow orchestration engines such as Apache Airflow or Google Cloud Composer.

Experience designing semantic layers and data exploration experiences in BI platforms such as Metabase, Tableau, or Looker.

Passion for mobile games and music technology.