Data Engineer

Aghanim is an integrated commerce, liveops automation, community engagement, and payments platform for video games.

Mobile games have traditionally depended on app stores for distribution, payments, and player relationships. We believe there is a better way. Aghanim helps game studios build direct relationships with players, sell directly, and build their future on their own terms. Today, more than 100 games worldwide are already building this future with Aghanim.

Our team brings together people across Los Angeles, New York, Seoul, Beijing, London, Lisbon, Belgrade and other locations around the globe, with deep expertise in gaming, fintech and technology. We move quickly, keep communication direct, and focus on getting things done. We believe the best people thrive when they have autonomy, ownership, and a stake in the company's success.

We are looking for a senior, hands-on data professional to help define how analytical data should be structured, governed, and consumed across our business.

This role sits at the intersection of product analytics, data governance, analytical modeling, and platform efficiency. The person in this role will be responsible for building the right abstraction layers in BigQuery, reducing reliance on raw-table querying, and creating trusted, reusable datasets for analytics, AI systems, and key reporting workflows.

The role will work closely with Customer Success, Product, AI/ML, Engineering, and Finance, with a particularly strong partnership with producers and teams working on monetization, behavioral analytics, e-commerce and payment-related performance.

1. Data Governance & Analytics Standards

• Own the governance of analytical data, including canonical datasets, metric definitions, dataset ownership, documentation, lineage, access controls, and data quality.

• Define and maintain shared business entities and reporting logic so teams work from consistent, trusted data.

• Partner with engineering teams to establish data contracts and improve the reliability of analytical datasets.

• Continuously identify recurring analytical workflows and convert them into reusable, governed data assets.

2. Analytics Data Modeling

• Design and maintain analytical data models in BigQuery across staging, core, and reporting layers.

• Build reusable datasets that support monetization, LiveOps, customer behavior, payments, and operational reporting.

• Ensure business logic is implemented at the appropriate layer and follows consistent modeling standards.

• Reduce direct querying of raw tables by providing well-structured analytical datasets.

3. Data Platform Performance & Scalability

• Optimize BigQuery performance and cost through efficient data modeling, partitioning, clustering, and query optimization.

• Define best practices for scalable analytical workloads and efficient data consumption.

• Identify redundant datasets and reporting logic, improving both maintainability and warehouse efficiency.

4. Semantic Layer & Analytics Architecture

• Help define the architecture for serving analytical data to BI tools, internal applications, and AI-driven workflows.

• Evaluate semantic-layer technologies and modern analytics architectures (such as Cube.js and similar solutions).

• Contribute to long-term decisions around analytical infrastructure and data serving patterns.

5. Cross-Functional Partnership

• Work closely with product, producers, analysts, and engineering teams to translate business requirements into reusable data models.

• Contribute hands-on using SQL, Python, BigQuery, dbt-style modeling practices, and GCP-native tooling.

• Improve how analytical work is structured across the organization and help establish best practices as the function grows.

Required Qualifications

• 5+ years of experience in data analytics, product analytics, analytics engineering, or a related function.

• Strong hands-on experience with BigQuery and GCP in production environments.

• Advanced SQL skills and strong practical experience building analytical models, reusable marts, or semantic/reporting-ready datasets.

• Hands-on experience using Python for analysis, validation, prototyping, or data workflow support.

• Proven experience with data governance in analytics environments, including several of the following:

  • source-of-truth design,

  • dataset ownership,

  • naming conventions,

  • documentation,

  • lineage,

  • access management,

  • data quality controls,

  • cost governance,

  • data contracts with engineering.

• Experience designing or improving data abstraction layers such as raw / staging / core / marts / semantic.

• Practical experience improving cost-efficiency and performance of analytical workloads in a cloud warehouse environment.

• Experience with semantic layers, OLAP / ROLAP / HOLAP architectures, or similar analytical serving patterns.

• Strong domain experience in at least one of the following areas:

  • monetization analytics,

  • LiveOps analytics,

  • e-commerce or payments analytics,

  • behavioral product analytics,

  • customer analytics.

• Experience working directly with cross-functional stakeholders and turning loosely defined requests into scalable analytical solutions.

• Demonstrated ability to stay hands-on while operating with senior ownership and strong execution discipline.

Preferred Qualifications

• Experience in gaming, mobile gaming, F2P, or live-service product environments.

• Experience with Looker or Looker Studio.

• Experience evaluating or implementing semantic-layer technologies such as Cube.js or equivalent solutions.

• Experience supporting AI/LLM-related data consumption use cases.

• Previous people management, mentoring, or team leadership experience.

• Experience helping define data standards across multiple teams rather than working only as an individual analyst.

• World-class team – work alongside experienced professionals from around the globe who have built products used by millions of players

• High growth, high impact – be part of a fast-growing company where ideas turn into products and reach customers in days, not months

• Autonomy and ownership – we trust people to make decisions, take initiative, and drive results

• Modern tools and technology – use AI, automation, and modern tools as part of your everyday work

• Equity – participate in the company's growth and long-term success