Data Analytics Engineer

Alchemy•New York, NY

17h

About The Position

This role sits at the frontier of how AI consumes and acts on internal company data. You'll be the data layer for the entire company — a strategic partner to Finance, Marketing, Sales, Product, and Data Science — designing the clean, trusted datasets that power our AI tooling and ensuring every team can make decisions from a single source of truth. You'll explore how technologies like MCP can unlock self-serve AI data access across the org, and ensure that as we adopt new AI capabilities, our data is ready to meet them. The rest of the company's AI ambitions run through you.

Requirements

6+ years in data engineering with strong SQL and deep Snowflake expertise
Experience designing efficient, scalable analytical data models
Proficiency with dbt or comparable transformation frameworks
Strong data quality instincts and a track record of building infrastructure that unblocks teams
Good judgment — you scope well, prioritize ruthlessly, and communicate tradeoffs

Nice To Haves

Experience in startups

Responsibilities

Build and own the canonical data models in Snowflake that serve as Alchemy's company-wide source of truth — clean, queryable, and AI-ready
Structure datasets so vendor AI tools perform optimally out of the box, with consistent schemas, rich semantics, and well-indexed access patterns
Explore and prototype MCP integrations that let internal teams query data conversationally
Eliminate shadow tables and one-off datasets by proactively serving team data needs at the platform level, freeing data science to move faster
Own the transformation layer (dbt), with rigorous testing and validation so every downstream consumer — human or AI — can trust what they're working with