Data Engineer

About the Company

Arena is the platform for evaluating how AI models perform in the real world. Founded by researchers from UC Berkeley's SkyLab, we're on a mission to measure and advance the frontier of AI for real-world use.

Responsibilities

• 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

Requirements

• 3+ years of experience in analytics engineering, data engineering, or a closely related role
• Strong proficiency in SQL with experience designing analytics-friendly schemas
• Hands-on experience with modern data warehouses (e.g., Databricks, Snowflake, BigQuery)
• Experience building and orchestrating data pipelines using Airflow or similar tools
• Proficiency in Python for data transformation and pipeline development
• Strong understanding of data modeling best practices
• Experience operating and debugging production data pipelines

Preferred Qualifications

• Experience with Spark or other distributed data processing frameworks
• Familiarity with Delta Lake or similar table formats
• Experience supporting experimentation, evaluation, or metrics-heavy products
• Exposure to machine learning systems or ML-adjacent analytics
• Experience improving data discovery, lineage, or documentation at scale