# Analytics Engineer

**Company:** [Arena](https://hotfix.jobs/companies/arena)
**Location:** San Francisco, CA, California
**Role:** Data Engineering
**Experience:** 3+ years
**Skills:** SQL, Python, Airflow, Databricks, Snowflake, BigQuery, Data Modeling, Data Pipelines, Spark, Delta Lake
**Posted:** 2026-02-03

> Designs and builds analytics data models, pipelines, and metrics from raw AI evaluation data. Partners with product and research teams to ensure scalable, high-quality data for insights and dashboards. Requires 3+ years experience with SQL, Python, Airflow, and modern data warehouses.

## Job Description

## 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 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 Haves

- 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

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