Architect Lead

Your Responsibilities

• Define and own the end-to-end architecture of the data platform.
• Establish the technical foundations, standards and best practices for a platform built from the ground up.
• Make, document and defend key architectural and technology decisions, balancing scalability, performance, cost and team capacity.
• Design solutions capable of supporting hundreds of millions of users and billions of events per day.
• Build core parts of the platform hands-on and remain actively involved in implementation.
• Partner with Product, Game, Analytics and Marketing leadership to translate business needs into a clear platform roadmap.
• Establish engineering practices, code review standards and operational discipline.
• Hire, mentor and grow the Data Engineering team.
• Ensure the platform is scalable, reliable, maintainable and cost-efficient.

What We're Looking For

• 7+ years of experience in Data Engineering, including experience as a Tech Lead, Architect, Staff or Principal Data Engineer.
• A proven track record of designing and building data platforms end-to-end.
• Strong architectural skills and the ability to make, document and defend technical decisions.
• Senior-level Python skills – must-have. You should be comfortable contributing directly to implementation.
• Strong SQL skills and a solid understanding of data modelling, data warehousing, schema design, partitioning and versioning.
• Strong experience with cloud platforms, preferably AWS, including technologies such as S3, Kinesis, ECS and Athena.
• Experience with modern data stack technologies, e.g. dbt.
• Hands-on experience with ETL orchestration tools such as Airflow or similar.
• Experience with Infrastructure as Code.
• Experience hiring, mentoring and developing engineering teams.
• Excellent communication and collaboration skills.

Nice to Have

Experience in gaming or other high-scale consumer environments will be highly valued, particularly experience with:

• billions of events,
• real-time or near-real-time data,
• large-scale consumer products,
• product analytics,
• LiveOps,
• high-volume data platforms.