Fortis Games-posted 2 days ago
Full-time • Director
251-500 employees

As the Director of Data Engineering at Fortis Games, you will lead the strategy, delivery, and evolution of our game-agnostic data platform — the foundation powering real-time personalization, analytics, and event-driven experiences for millions of players worldwide. Your first-year mission: deliver a scalable, self-service data platform that supports 100M+ players with sub-100ms ingestion latency — enabling game teams to ship features, run experiments, and generate insights faster without backend friction. You’ll oversee the Data Engineering group, covering data ingestion, analytics SDKs, APIs, and ML infrastructure. Your mandate is to ensure Fortis’ data systems are reliable, cost-efficient, and developer-friendly, while championing excellence in distributed systems, data software design, and observability. This is a highly collaborative leadership role: you’ll work closely with Data Science, Machine Learning, Analytics, and Game Development teams to translate gameplay and business needs into scalable, production-grade data services that enable experimentation, personalization, and deep player insight.

  • Lead and grow a high-performing Data Software Engineering team, fostering a culture of technical excellence and ownership.
  • Define and execute a three-year data systems vision and 12-month roadmap aligned with Fortis’ personalization strategy.
  • Architect and scale real-time data processing and API-based systems using tools such as Redpanda, Flink, ClickHouse, and Iceberg.
  • Partner cross-functionally with Game Development, Data Science, and Analytics to deliver end-to-end data workflows.
  • Automate pipelines and improve developer velocity by removing operational bottlenecks.
  • Establish and track key metrics, including uptime, latency, and cost per million events.
  • Champion reliability, scalability, and developer experience, driving adoption across all Fortis studios.
  • Ensure data privacy and compliance with global standards (GDPR, CCPA) across all standards (GDPR, CCPA).
  • Proven leadership of Data or Software Engineering teams building large-scale, real-time data systems.
  • Deep technical expertise in distributed data processing, event streaming, and API design.
  • Experience with pub/sub messaging systems (Kafka, Redpanda, Pulsar) and workflow orchestration (Airflow, Dagster).
  • Cloud-native architecture proficiency across AWS, GCP, or Azure.
  • Strong understanding of ML infrastructure and pipelines—enabling ML engineers and data scientists.
  • Demonstrated ability to balance long-term architectural vision with rapid delivery cycles.
  • Excellent communication and cross-functional influence skills.
  • Commitment to data quality, observability, and governance best practices.
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