Staff Software Engineer - Enterprise Data & AI (Intelligence Platform)

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 build the governed data foundations that power enterprise analytics, automation, and applied AI. Your primary focus will be data architecture and data engineering, where you will be responsible for designing and operating production data pipelines in Databricks, integrating & curating data through a medallion architecture, and defining semantic and domain models in Unity Catalog with subject-matter experts.

You will also build agents and retrieval-augmented applications directly on this governed data using Databricks capabilities such as Unity Catalog functions, Mosaic AI, and model serving. You will own your systems from architecture through production operations, applying strong engineering, data quality, security, evaluation, and observability practices throughout

The ideal candidate is a hands-on data and software engineer who can turn ambiguous enterprise needs into reliable data products and focused AI-enabled solutions.

The role will report to the Senior Manager, Enterprise Data and AI.

Responsibilities:

Data Pipelines and Semantic Layer (Primary)

• Build and operate pipelines that ingest source-system data into Databricks using a medallion architecture across bronze, silver, and gold layers, with lineage, provenance, access controls, and logging.

• Partner with subject-matter experts to define and evolve domain, ontology, and semantic models in Unity Catalog, then curate reliable data products into those models.

• Own data quality for the pipelines and products you build, implementing tests, monitoring, and alerting so issues surface before they reach downstream consumers.

• Integrate enterprise systems through APIs, events, and middleware while applying appropriate authentication, retries, reconciliation, and auditability.

Agents on the Databricks Data Layer (Secondary)

• Build production agents and retrieval-augmented applications grounded in governed Databricks data, using Unity Catalog functions as tools and Mosaic AI or model-serving capabilities.

• Add authorization, approvals, and auditability for consequential actions performed through agents or AI-enabled orchestrations.

• Create evaluation sets and observability for the agents you ship, including groundedness, latency, cost, and production feedback, and use the results to improve them.

Engineering Fundamentals

• Apply solid engineering practices, including automated testing, code review, CI/CD, documentation, on-call support, and concise design notes.