Staff Data and AI Engineer - Enterprise

Riot Games•Los Angeles, CA

4h•$161,500 - $227,000

About The Position

Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential, from efficient business platforms to next-generation AI capabilities. As a Staff Data and AI Engineer on the Enterprise Data & AI team, you will operate at the intersection of data engineering, data architecture, and AI solutions for Riot Enterprise. You will be instrumental in designing and building the enterprise data warehouse, developing scalable data integrations (connecting platforms like OneStream, Oracle, Workday, and Databricks), and delivering critical Enterprise agentic analytics and AI services. The ideal candidate blends hands-on technical expertise with a strategic, platform-oriented mindset. You will be responsible for creating robust technical solutions, including scalable data pipelines, comprehensive data warehouse models, and AI enabled workflow and process automations. This work involves close collaboration with Enterprise leaders, architects, and system owners, and data specialists to translate corporate strategic and operations business requirements into secure, high-value, and maintainable solutions.

Requirements

Bachelor’s degree in Computer Engineering, Computer Science, Information Systems, or related field (or equivalent experience delivering enterprise technology solutions).
8+ years of experience in enterprise applications, data engineering, or platform engineering spaces, including delivery of data and automation solutions at scale.
Experience delivering AI (traditional or generative) or advanced agentic automation solutions at scale.
Strong technical knowledge of orchestration platforms (Workato, n8n, Camunda) and AI platforms (e.g., Google Vertex/Agent Platform, Anthropic Claude Platform, OpenAI Platform) and their enterprise applications, including Retrieval-Augmented Generation (RAG), as well as knowledge management, data governance, quality, and architecture in enterprise contexts.
Hands-on experience building or supporting data pipelines and integrations across enterprise applications, APIs, middleware, and data platforms.
Required hands-on experience with Databricks for developing, transforming, and operationalizing data workflows or warehouse-oriented solutions.
Strong familiarity with data transformation and data warehousing methodologies, including dimensional modeling and medallion architecture patterns.
Strong technical knowledge of enterprise platforms (e.g., Workday, ServiceNow, Coupa, Concur, Oracle, IronClad) and respective integration options.

Nice To Haves

Experience supporting master data, reporting, analytics, or business intelligence use cases.
Strong hands-on experience with Workato, including implementing agentic workflows and AI-powered operational analytics and automations.
Familiarity with frameworks for agent development (e.g., LangChain, Pydantic AI) and orchestration platforms (e.g., LangGraph, CrewAI, Vertex AI Agent Builder, Microsoft AutoGen, or similar).
Experience building or integrating enterprise knowledge search platforms (e.g., federated search, vector DBs, retrieval-augmented generation) for enterprise use cases.
Experience with cloud data and application technologies such as AWS data services, dbt, or event-driven integration patterns.
Experience leading change in complex organizations and mentoring teammates or helping less technical partners adopt better data and systems practices.

Responsibilities

Partner with stakeholders across Finance, HR, Legal, IT, and Operations to understand business workflows, clarify requirements, and translate them into technical designs, with an immediate focus on process automation, conversational agents, and data integrations for key corporate systems.
Design, build, and maintain the data and platform components of Riot’s Enterprise AI ecosystem, contributing to reference architectures for RAG/agents (indexing, vector stores, caching) and orchestration frameworks.
Contribute to conceptual and logical data models for enterprise domains and help connect those models to business capabilities, processes, and downstream applications.
Design, build, and maintain data pipelines, transformations, integrations, and warehouse-oriented data products across enterprise platforms and data systems.
Support reporting, analytics, and application capabilities by helping shape reliable data foundations and implementation patterns.