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Benchling

Data Engineer

Remote, US (Remote)$83k–$207kfulltimemidAdded today

About this role

Benchling seeks a Data Engineer to build and operate production-grade data pipelines and warehouse infrastructure supporting company-wide analytics and AI initiatives. You'll own end-to-end ELT pipelines, ensure data governance and quality, and partner with AI engineering teams to enable trustworthy data for internal AI applications.

What you'll do

  • Design and operate production ELT pipelines ingesting data from Benchling product, Salesforce, and third-party systems into Snowflake with dbt modeling
  • Maintain data quality, monitoring, testing, and schema versioning standards as usage scales across the organization
  • Manage Snowflake access controls, PII handling, data governance policies, and warehouse cost optimization
  • Support AIDE's AI engineering initiatives by providing governed, trustworthy datasets for agentic AI tooling and internal applications
  • Contribute to architectural decisions on warehouse design, semantic layers, and metrics store strategy
  • Monitor pipeline health and uptime, ensuring reliable data delivery to GTM, Customer Success, Product, and Finance teams

What they're looking for

  • SQL and Python
  • dbt and data modeling
  • Snowflake or modern cloud data warehouse
  • Orchestration tools (Airflow or equivalent)
  • Cloud infrastructure (AWS or similar)
  • Software engineering practices (version control, CI/CD, testing)
  • Data governance and PII handling
  • ELT pipeline design and implementation
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Benchling

Benchling builds an AI-powered platform for biotech R&D that integrates scientific workflows and data processes to accelerate research breakthroughs. The company is hiring software engineers across full-stack, customer engineering, agentic AI, and security roles to enhance developer productivity, build production AI systems, and protect sensitive research data.

Website
benchling.com
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Likely interview questions

  • Walk us through a production data pipeline you've owned end-to-end—what were the ingestion, transformation, and modeling challenges, and how did you handle them at scale?
  • Describe your experience with dbt and how you've applied software engineering practices like testing and CI/CD to data models.
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