Data Engineer

Reposted 19 Days Ago
7 Locations
Remote or Hybrid
Mid level
Artificial Intelligence • Information Technology • Software
The Role
The Analytics Engineer will design data models, build pipelines, and ensure data quality to support AI evaluation and insights.
Summary Generated by Built In
About Arena Intelligence

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, and to build the foundation for everyone to understand, shape, and benefit from it.


Tens of millions of people use Arena each month to evaluate how frontier systems handle the work they actually do. The preferences they share power the most transparent, rigorous, and human-centered evaluations in AI. Leading AI labs, enterprises, and independent researchers rely on our work and open datasets to understand how models behave in real workflows: agentic coding, creative generation, professional productivity, and beyond. We go beyond leaderboards and decompose what human experience reveals about AI, so models advance toward the work people actually do.


We're a team of researchers, academics, builders, and creatives from UC Berkeley, Google, Stanford, and DeepMind. 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 together, in an office culture that radiates excellence, energy, and focus.

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

What we offer
  • We offer competitive compensation and equity aligned to the markets where our team members are based. The base salary range will depend on the candidate’s permanent work location.

  • Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.

  • The opportunity to work on cutting-edge AI with a small, mission-driven team

  • A culture that values transparency, trust, and community impact

Come help build the space where anyone can explore and help shape the future of AI.

Arena Intelligence provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.

Skills Required

  • 3+ years of experience in analytics engineering, data engineering, or related role
  • Strong proficiency in SQL
  • Hands-on experience with a modern data warehouse
  • Experience building data pipelines using Airflow
  • Proficiency in Python for data transformation
  • Strong understanding of data modeling best practices
  • Experience operating production data pipelines
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The Company
HQ: San Francisco, California
58 Employees
Year Founded: 2025

What We Do

Created by researchers from UC Berkeley, Arena (formerly LMArena) is a community-powered platform for understanding AI performance in the real world. Tens of millions of builders, researchers, and creative professionals come to Arena to use frontier models and give feedback on their responses, shaping a public leaderboard grounded in real-world use.

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