# Data Engineer, People Innovation Labs

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Data Engineering
**Salary:** $293k – $325k/yr
**Experience:** 3+ years
**Skills:** Python, Scala, Java, Databricks, Snowflake, Fivetran, Airflow, Dagster, Prefect, Spark, Hadoop, Flink, Hdfs, S3
**Posted:** 2026-05-13

> Build and manage data pipelines for people analytics and internal products like OpenHouse at OpenAI's People Innovation Labs. Collaborate with analytics and engineering teams using Databricks, Spark, and ETL tools; requires 3+ years data engineering experience.

## Job Description

## Responsibilities
- Design, build and manage people data pipelines, ensuring all data is seamlessly integrated into our Databricks warehouse.
- Develop canonical datasets to track key people metrics and People Innovation Labs product metrics.
- Work collaboratively with various teams, including Data Platform, Data Science, People Analytics, and Compensation and Equity to understand their data needs and provide solutions.
- Implement robust and fault-tolerant systems for data ingestion and processing.
- Participate in data architecture and engineering decisions, bringing your strong experience and knowledge to bear as the primary data engineering expert on the team.
- Ensure the security, integrity, and compliance of data according to industry and company standards.

## Requirements
- 3+ years of experience as a data engineer and 8+ years of any software engineering experience (including data engineering).
- Proficiency in at least one programming language commonly used within Data Engineering, such as **Python**, **Scala**, or **Java**.
- Experience with data warehousing technologies such as **Databricks** and **Snowflake**, and expertise with ETL schedulers such as **Fivetran**, **Airflow**, **Dagster**, **Prefect**, or similar.
- Experience with distributed processing technologies and frameworks, such as **Spark**, **Hadoop**, **Flink** and distributed storage systems (e.g., **HDFS**, **S3**).

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