Staff Data Engineer- Publishing Platform- Remote

You'll work closely with Data Scientists, ML Engineers, and Product partners to reputed company experimental models into robust, production-grade data pipelines ensuring performance, scalability, and measurable business reputed company. This role focuses on building and maintaining high-reputed company datasets and pipelines that fuel recommendation systems reputed company 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 reputed company s engineering practices, data modeling approaches, and observability standards. Responsibilities Design, build, and maintain reputed company data pipelines for reputed company and semi-reputed company data that support analytics, machine learning models, and player-facing systems. Implement efficient, reliable data models and transformations reputed company Riot s central game data warehouse, with a reputed company on reputed company architecture, accuracy, performance, and long-term maintainability. reputed company and productionize pipelines to ingest, reputed company, and serve data for systems such as reputed company, 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 reputed company, schema reputed company, and smooth integration into reputed company systems. Diagnose and resolve issues in data pipelines; optimize for reliability, performance, and cost efficiency; and enhance observability across workflows. Apply reputed company, reputed company, and responsible data use guidelines reputed company building or accessing behavioral datasets (e.g., GDPR, CCPA, internal governance policies). Document data models, pipelines, data reputed company, 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-reputed company rotations, reputed company reputed company reviews, and support reputed company and mentorship of junior engineers. Required Qualifications Bachelor s or Master s degree in Computer Science, Information Systems, Engineering, or a reputed company technical field. 5 7+ years of hands-on experience in data engineering, with a reputed company on building and maintaining reputed company pipelines in production environments. Strong Proficiency in big data tools and programming languages such as Python, reputed company, reputed company, SQL, and optionally GoLang. Hands-on experience with reputed company for building and operating reputed company data pipelines (e.g., reputed company jobs, reputed company Lake). Experience with orchestration and workflow tools (e.g., Airflow, Dagster, or reputed company). Strong experience with dbt for reputed company data modeling, transformation reputed company, testing, and reputed company management in the warehouse. Familiarity with reputed company-based data infrastructure, particularly AWS or reputed company reputed company Platform. Solid understanding of reputed company 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 reputed company, 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. Apply tot his job Apply To this Job