About the role
About Arena Intelligence Arena Intelligence is the open platform for evaluating how AI models perform in the real world. Created by researchers from UC Berkeley’s SkyLab, our mission is to measure and advance the frontier of AI for real-world use. Millions of people use Arena Intelligence each month to explore how frontier systems perform — and we use our community’s feedback to build transparent, rigorous, and human-centered model evaluations. Leading enterprises and AI labs rely on our evaluations to understand real-world reliability, alignment, and impact. Our leaderboards are the gold standard for AI performance — trusted by leaders across the AI community and shaping the global conversation on model reliability and progress. We’re a team of researchers, engineers, academics, and builders from places like UC Berkeley, Google, Stanford, DeepMind, and Discord. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We’re building a company where thoughtful, curious people from all backgrounds can do their best work. Everyone on our team is a deep expert in their field — our office radiates excellence, energy, and focus.
About
the Role Arena is seeking an experienced Analytics Engineer to own the data foundations that power real-world AI evaluation. In this role, you will design and build the analytics-layer data models, pipelines, and metrics that turn raw user activity and votes into trusted insights for the public, AI labs, and enterprise customers. This role sits at the intersection of data engineering, analytics, and product . You’ll work closely with researchers, product managers, and engineers to define schemas, standardize metrics, and ensure that our evaluation data is accurate, interpretable, and scalable. Your work will directly shape how AI performance is measured, understood, and acted upon across the industry. This is an ideal role for someone who enjoys building clean, well-modeled data systems , cares deeply about data quality and correctness, and wants to see their work influence both product decisions and external customers. You’ll Own the design and implementation of analytics-ready data models, schemas, and tables in our data warehouse Build and maintain reliable data pipelines (batch and incremental) that transform raw event and vote data into standardized, trusted datasets Define and standardize core metrics used across product, research, and customer-facing evaluations Partner with product managers and researchers to translate evaluation questions into robust data models Develop and maintain dashboards, reports, and data artifacts used by internal teams and external partners Ensure data quality through testing, validation, monitoring, and documentation Orchestrate and schedule data workflows using Airflow or equivalent tools Optimize queries and pipelines to support large-scale analytical workloads Contribute to improving data discoverability, lineage, and documentation across the warehouse You’ll have 3+ years of experience in analytics engineering, data engineering, or a closely related role Strong proficiency in SQL , with experience designing analytics-friendly schemas and transformations Hands-on experience working with a modern data warehouse (e.g., Databricks, Snowflake, BigQuery) Experience building and orchestrating data pipelines using Airflow or similar workflow orchestration tools Proficiency in Python for data transformation, validation, and pipeline development A strong understanding of data modeling best practices (e.g., dimensional modeling, metrics layers) Experience operating and debugging production data pipelines with a focus on correctness and reliability