Lead Data Engineer

About the role

At Kabam, we’re constantly raising the bar of excellence in free-to-play gaming. We hire for passion and diversity so that we can triumph in our collective skill. Our teams are made up of a fusion of personalities and interests bound together by a collaborative nature, fun-loving culture, and a drive to do what it takes to make great games. And this is where you come in…

The Lead Data Engineer owns Kabam's central data platform end to end, from the game telemetry standard through to the certified data layer that analytics, finance, data science and AI tools are built on. They lead the data engineering team and are accountable for the reliability, quality and cost of petabyte-scale pipelines across GCP and Databricks. They set the standards for how data enters and moves through the platform, and work directly with game, backend and analytics leads to deliver against company priorities.

This role is a full-time, on-site position based 5 days a week at our Downtown Vancouver studio.

In this role, you can expect to: Key Accountabilities

Team Leadership:

- Lead and manage the data engineering team: day-to-day direction, performance management, and development.

- Own the data engineering roadmap and prioritise work against company and data priorities.

- Provide technical mentorship and elevate quality through code and architectural reviews.

Data Standards & Upstream Ownership:

- Resolve data-definition and implementation questions directly with backend and game leads.

- Define and enforce model contracts, testing and certification so downstream teams can rely on the data.

Data Architecture & Platform:

- Design and evolve the data architecture (lakehouse and medallion layers across GCS, Databricks and BigQuery) for reliability, consistency and efficiency.

- Evaluate and choose technologies and vendors based on value, risk and migration cost.

- Lead platform migrations and modernisation of legacy pipelines.

- Explain architectural decisions and trade-offs clearly to non-technical stakeholders.

Cost & Efficiency:

- Accountable for data platform spend, including BigQuery and Databricks.

- Identify and deliver cost and efficiency improvements across the data platform, balancing spend against performance and reliability.

- Report cost and efficiency trends to data leadership.

Data Development & Operations:

- Build and maintain data pipelines to a high engineering standard using dbt, orchestration tools and devops principles.

- Own data reliability: SLAs, monitoring, incident response, and clear communication of impact and timelines during incidents.

- Identify and prioritise technical debt against delivery work.

- Build data quality monitoring and alerting so issues are caught before stakeholders see them.

Data Governance & AI Readiness: