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Cohere

Data Engineer, Data Foundations

New York (Remote)fulltimemidAdded 1 month ago

About this role

Cohere seeks a Data Engineer to build foundational data infrastructure supporting the company's enterprise AI platform. You'll design and maintain production-grade data systems that power analytics, customer experiences, and strategic decision-making across the organization.

What you'll do

  • Design and implement production-grade data processing systems handling unstructured and structured data at scale
  • Build end-to-end data pipelines and ETL workflows using distributed processing frameworks
  • Collaborate with researchers, engineers, and business teams to translate data insights into product and strategy recommendations
  • Develop performant datasets in relational databases and cloud storage systems
  • Maintain and optimize modern analytics infrastructure and tooling
  • Own implementations from conception through delivery and measurable outcomes

What they're looking for

  • Python and SQL
  • Apache Beam, Spark, or Flink
  • BigQuery, Airflow, or dbt
  • Large-scale data system design
  • Data transformation and pipeline development
  • Java or Golang (preferred)
  • Kubernetes (preferred)
  • AI/ML domain knowledge

Benefits

  • $75 weekly lunch stipend
  • Comprehensive health, dental, and mental health coverage
  • 6 weeks paid vacation (30 working days)
  • 100% parental leave top-up for up to 6 months
  • Education and learning stipend for professional development
  • Home office stipend ($500) and remote-friendly work environment
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Cohere

Cohere builds enterprise AI systems and large language model infrastructure, including audio inference optimization, agentic AI workflows, and petabyte-scale data infrastructure for model training. The company is hiring software engineers, infrastructure specialists, forward-deployed engineers to work with enterprise customers, and IT support staff.

Website
cohere.com
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Likely interview questions

  • Walk us through a production data processing system you've built. What framework did you use (Spark, Beam, Flink), and how did you handle scale?
  • Describe your experience transforming unstructured data into performant relational or blob storage datasets. What were the key challenges?
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