Engineering Manager, Data Platform
Alloy · New York City
About this role
## Alloy — Engineering Manager, Data Platform Alloy is the AI-powered identity and fraud prevention platform that accelerates onboarding, stops fraud, and scales compliance across the customer lifecycle so financial organizations can grow without limits. More than 900 of the world’s leading financial institutions and fintechs trust Alloy for smarter risk management that drives growth. Through our values—**Be Bold, Go Fast, Collaborate, and Celebrate Our Differences**—we’re building a workplace where you can grow, thrive, and belong. --- ## About the team The **Data Platform** team sits within Alloy’s Intelligence vertical and owns the infrastructure that powers how data is modeled, governed, and delivered—internally and to customers. We work at the intersection of data engineering and analytical depth, with a stack built around **dbt**, **Snowflake**, and **Artie**, plus a growing investment in our **semantic layer**. This is an early but high-leverage moment for Analytics Engineering at Alloy. The role is central to establishing the patterns, standards, and culture of Analytics Engineering. Alloy operates in a **hybrid-work** environment, with local employees onsite **three days a week**. --- ## What you’ll do - **Manage the Data Platform team**: hire, grow the people, and set direction (player-coach; ~half hands-on, ~half leading) - Lead a team of **two engineers**: set direction, run 1:1s and performance reviews, give direct feedback, and coach toward stretch - **Hire as the team grows**: define roles, run interview loops, and decide who joins - **Own the data platform end to end**: ingestion & CDC, the Snowflake warehouse, dbt & the semantic layer, and the tooling that powers internal and customer-facing analytics - **Ship native warehouse data sharing** into customers’ environments, including the **schema contracts** they’ll build on for years - Design how **sensitive customer data** is handled across analytics and BI so teams get answers while customer data stays protected - Set the team’s technical standard for **modeling, testing, documentation, and code review**—and meet it in your own code - Partner with **Product, Compliance, and engineering** to decide what data we expose, to whom, and under what constraints --- ## What we’re looking for You want both halves of this job: building and growing people. - Deep **analytics engineering** and **data systems** experience - Strong **SQL**, **dbt**, and a cloud data warehouse (Snowflake preferred) - Real depth in data modeling (grain, slowly changing dimensions, and what makes a model trustworthy) - Experience owning architecture from **ingestion → warehouse → delivery**, including CDC tools (Artie or similar) and how they fail (late-arriving data, schema drift, out-of-order events) - A record of **leading people** (formal management experience not required) - You’ve set direction, grown someone on purpose, given hard feedback, and raised team standards - **Good judgment about sequencing**: you pick what comes first and can explain why - Experience driving adoption across teams or aligning on constraints while keeping relationships intact - Based in the **New York City area** and able to work from the **Union Square office Tuesday–Thursday** ### Nice-to-haves - Native warehouse data delivery (e.g., Snowflake Data Sharing / Marketplace) - Fintech / financial services / regulated industry experience, especially handling **PII** in analytics - Experience building a
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