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Alchemy

Data Analytics Engineer

San Francisco (Remote)$200k–$240kfulltimemidAdded 1 month ago

About this role

Join Alchemy as a Data Analytics Engineer to build the foundational data infrastructure powering company-wide AI initiatives. You'll design canonical data models in Snowflake, implement transformation pipelines with dbt, and enable self-serve AI data access across Finance, Marketing, Sales, Product, and Data Science teams.

What you'll do

  • Build and maintain canonical data models in Snowflake serving as the company's single source of truth
  • Design AI-ready datasets with optimal schemas and indexing for vendor AI tools
  • Prototype and implement MCP integrations for conversational data querying
  • Own the dbt transformation layer with rigorous testing and data quality validation
  • Eliminate shadow datasets by proactively serving team data needs at platform level
  • Partner strategically with cross-functional teams to unblock analytics and data science workflows

What they're looking for

  • SQL and Snowflake (expert level)
  • dbt or comparable transformation frameworks
  • Analytical data modeling and schema design
  • Data quality and validation practices
  • MCP (Model Context Protocol) integration
  • Communication and cross-functional collaboration
  • Startup experience
  • Data infrastructure and platform thinking

Benefits

  • Medical, dental, and vision coverage
  • Home office build-out budget
  • Gym reimbursement
  • Flexible time off
  • Learning and development stipend
  • Wellness and mental health support, HSA/FSA plans, fertility benefits
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Alchemy

Alchemy builds a blockchain developer platform that provides infrastructure and services for crypto and wallet applications, handling billions of daily requests. The company is hiring software engineers, infrastructure specialists, data engineers, and customer-facing technical roles to support its platform scaling, customer success, and internal AI and data initiatives.

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Likely interview questions

  • Walk us through how you've designed a canonical data model in Snowflake from scratch. What was your approach to ensuring it served multiple downstream teams without creating silos?
  • Describe your experience with dbt. How do you approach testing and validation to ensure data quality that both analysts and AI systems can trust?
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