Data Engineer

About the Role

Arena Intelligence is seeking an experienced Data 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

Nice to have's

• 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