Lead Data Engineer

About Us 🚀

Immutable is the world’s fastest-growing game platform, built to power the next generation of games. We aim to drive the next evolution of gaming, combining performance, digital ownership and cutting-edge technology to help games grow and succeed. At the intersection of AI-driven analytics, high-performance growth tools, and seamless digital ownership, our technology helps studios attract, engage, and scale player communities.

Founded in 2018, Immutable is one of Australia’s fastest companies to reach unicorn status, raising more than AUD $400M+ with a valuation of AUD $3.5B. Today, more than 700 games are building on Immutable, and industry leaders like Ubisoft have chosen us as their partner for the future of gaming.

Over 6 million players use Immutable Passport, our unified identity enabling frictionless cross-game experiences, and our Immutable Audience platform has become the fastest-growing SaaS product line in our history. We're scaling rapidly, backed by a global team of creators, engineers, and gamers united by one mission: to build the most valuable growth layer in gaming and redefine how games launch, scale, and succeed. 🚀

A bit about the role 🤔 Our fastest-growing product is Immutable Audience, an AI-native marketing and growth platform that helps game studios find, understand and keep their players. Every part of that product runs on data, and right now the data team is hiring Lead Data Engineer to support the whole path data takes, ingestion, transformation, modelling and getting it in front of internal and external customers.

Build a greenfield real-time event-streaming platform on Kafka, Flink, and ClickHouse, serving segmentation and targeting at scale Set the technical direction for the platform as our products scale Own warehouse datasets and transformations end to end in BigQuery, dbt, SQL, and Python Set data quality, testing, and observability standards across streaming and batch, tuning for cost and latency as volumes scale Raise the bar through code review, mentoring, and patterns the team builds on Build AI into how you work and into the platform itself, from coding agents to automated quality checks

Strong, hands-on data engineering fundamentals: pipelines, modelling, and clear reasoning about correctness, scale, and failure modes Strong SQL and Python, used daily in production Experience designing, building, and scaling production data pipelines end to end (some streaming exposure is a plus) Depth in the modern batch stack: dbt, an orchestrator, a cloud warehouse Data modelling skills, schemas that serve analytics and product use cases and hold up at scale Real experience with event data at billions-of-rows scale Experience with cloud infrastructure (AWS or GCP) and infra-as-code A data quality and observability mindset, testing, monitoring, and alerting as habit A track record of setting technical direction through architecture decisions and mentoring senior engineers Clear communication, you can explain data trade-offs to non-technical stakeholders Strong ownership and pragmatic judgment Comfort with ambiguity, priorities shift as the product evolves and you adapt without a perfect spec Our technical stack: AWS, GCP BigQuery, dbt, a new Kafka/Flink/ClickHouse event-stream pipeline, and AI tooling including Cursor and Claude Code.

Shipped streaming systems in production with tools like Kafka and Flink, ideally alongside an OLAP store like ClickHouse Background in gaming, adtech, or martech event data A track record of using AI to multiply your output and lifting your team's AI fluency

laptop-ats-crawler v3