Staff Data Engineer- Publishing Platform- Remote

You'll work closely with Data Scientists, ML Engineers, and Product partners to transform experimental models into robust, production-grade data pipelines ensuring performance, scalability, and measurable business impact.

This role focuses on building and maintaining high-quality datasets and pipelines that fuel recommendation systems within player platforms, supporting data-driven personalization across store content, promotions, and more. We are looking for an individual who operates with a high degree of autonomy and proactively anticipates and mitigates risks. You ll also contribute to modernizing our data systems and improving the team s engineering practices, data modeling approaches, and observability standards.

Responsibilities

• Design, build, and maintain scalable data pipelines for structured and semi-structured data that support analytics, machine learning models, and player-facing systems.
• Implement efficient, reliable data models and transformations within Riot s central game data warehouse, with a focus on Medallion architecture, accuracy, performance, and long-term maintainability.
• Develop and productionize pipelines to ingest, transform, and serve data for systems such as Client, payments, content delivery, and store recommendations including instrumentation to support A/B testing and key performance metrics.
• Collaborate with Data Scientists, Machine Learning Engineers, and Software Engineers to ensure data quality, schema clarity, and smooth integration into downstream systems.
• Diagnose and resolve issues in data pipelines; optimize for reliability, performance, and cost efficiency; and enhance observability across workflows.
• Apply privacy, security, and responsible data use guidelines when building or accessing behavioral datasets (e.g., GDPR, CCPA, internal governance policies).
• Document data models, pipelines, data contracts, and SLAs to ensure transparency and alignment across teams.
• Contribute to team engineering practices, including coding standards, testing strategies, and operational best practices.
• Participate in on-call rotations, perform code reviews, and support onboarding and mentorship of junior engineers.

Required Qualifications

• Bachelor s or Master s degree in Computer Science, Information Systems, Engineering, or a related technical field.
• 5 7+ years of hands-on experience in data engineering, with a focus on building and maintaining scalable pipelines in production environments.
• Strong Proficiency in big data tools and programming languages such as Python, Scala, Spark, SQL, and optionally GoLang.
• Hands-on experience with Databricks for building and operating scalable data pipelines (e.g., Spark jobs, Delta Lake).
• Experience with orchestration and workflow tools (e.g., Airflow, Dagster, or Prefect).
• Strong experience with dbt for modular data modeling, transformation framework, testing, and lineage management in the warehouse.
• Familiarity with cloud-based data infrastructure, particularly AWS or Google Cloud Platform.
• Solid understanding of Medallion architecture, schema design and data modeling principles for analytical and operational use cases.
• Exposure to streaming data pipelines or event-driven ingestion using technologies like Kafka, Kinesis, or Pub/Sub.
• Working knowledge of data quality, testing, version control, and observability best practices in modern data workflows.
• Strong collaboration skills with the ability to communicate effectively across engineering, analytics, and product teams.

Desired Qualifications

• Experience supporting personalization, recommendations, or player-facing ML systems.
• Exposure to feature stores, ML data pipelines, or online/offline data management patterns.
• Knowledge of event-based or contextual recommendation signals (user behavior, session data, content metadata).
• Interest in contributing to data architecture and standards as an emerging craft leader.