Staff Data Engineer, Analytics at CharacterAI
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

26 Jul, 25

Salary

250000.0

Posted On

27 Apr, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Spark, Data Engineering, Data Warehousing, Dbt, Collaboration, Pipelines, Social Media, Airflow, Processing

Industry

Information Technology/IT

Description

REQUIREMENTS:

  • 5+ years experience in data engineering, preferred within a consumer-facing technology company (chat, social media, or UGC)
  • Experience building data warehousing and pipelines with BigQuery, Spark, DBT and Airflow
  • Experience with processing and transforming unstructured data sources.
  • Experience modeling and scaling event telemetry systems for analytical use-cases
  • Experience implementing and supporting product experimentation data platforms
  • Proven track record of cross-functional execution and collaboration
  • Strong Python and SQL experienceLOCATIONS:
  • NYC Preferred, SF Bay Area OK
Responsibilities

As Staff Data Engineer, you will work on a diverse set of initiatives spanning the data engineering and data science domains to help Character grow its product through analytical excellence.

You will partner with our Data Platform and Data Science teams to optimize warehouse design and performance, evolve critical product analytics systems, enable and expand use cases of product data and help develop a world-class data culture. Your initial focus will be on these key areas:

  • Enable product team insights: Design, implement, and maintain a robust data warehousing design for consistent and reliable reporting and ease of data exploration as we continue to better understand our users
  • Own the core company data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Character
  • Evolve data model and data schema based on business and engineering needs, help character adopt best practices
  • Implement and adopt systems tracking data quality and consistency, improve pipeline performance and reliability
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