Senior Analytics Engineer

The Data Platform team sits within Alloy's Intelligence vertical and owns the infrastructure that powers how data is modeled, governed, and delivered - both internally and to customers. We work at the intersection of data engineering and analytical depth, with a stack built around dbt, Snowflake, and Artie, and a growing investment in our semantic layer.

This is an early but high-leverage moment for Analytics Engineering at Alloy. We have the tooling, the data, and the leadership experience to build this function the right way - and this role is central to that effort. The person who joins will help establish the patterns, standards, and culture of Analytics Engineering here.

The strategic stakes are real: native warehouse data delivery is becoming a core part of how we serve customers, agentic workflows depend on a well-maintained semantic layer, and OLAP infrastructure is increasingly woven into our product stack. Analytics Engineering sits at the center of all three.

As a Senior Analytics Engineer, you will be a technical anchor for how Alloy models, governs, and exposes data. You'll work closely with Data Science, Product, Engineering, and client-facing teams to ensure our data assets are trustworthy, well-documented, and built for scale.

- Design and build robust dbt models that serve as the authoritative foundation for analytics, machine learning features, and customer-facing data products.

- Own and evolve our semantic layer defining metrics, dimensions, and business logic in a way that supports both internal consumers and emerging agentic tooling.

- Partner with Engineering and Data Science to ensure our Snowflake data warehouse is well-structured, performant, and aligned with product needs.

- Establish and champion best practices for data modeling, testing, documentation, and code review across the team.

- Collaborate with client-facing and product teams to scope and deliver native warehouse data delivery to customers.

- Identify and address data quality issues proactively, building the observability and governance frameworks that keep data trustworthy at scale.

- Influence how Analytics Engineering is practiced at Alloy-this is a greenfield opportunity to set the standard.

We're looking for a Senior Analytics Engineer who combines deep technical craft with the instincts of a cross-functional partner. You don't just model data-you think about how it will be used, by whom, and what it needs to look like to be genuinely useful. An ideal candidate has:

- 5+ years of experience in analytics engineering, data engineering, or a closely related role, with a strong command of dbt and SQL.

- Hands-on experience with Snowflake or a comparable cloud data warehouse, including performance tuning and warehouse design.

- Experience building or maintaining a semantic layer or metrics layer (e.g., dbt Semantic Layer, MetricFlow, or similar).

- A strong sense of data modeling fundamentals. You have opinions about when to denormalize, how to handle slowly changing dimensions, and what makes a model trustworthy.

- Familiarity with data ingestion and CDC tooling; experience with Artie or similar streaming/replication tools is a plus.

- The ability to partner effectively with Data Science, Engineering, and Product. You translate between technical and non-technical stakeholders without losing precision.

- Experience establishing standards: testing frameworks, documentation practices, naming conventions, and review processes that teams actually follow.

- Comfort working in an environment where the function is still being shaped-you see that as opportunity, not ambiguity.

- Someone who embodies our shared Alloy values: be bold, get scrappy, collaborate, and celebrate our differences.

- Must be local to New York City; hybrid work with Tuesday-Thursday in-office at our Union Square HQ.

- Experience with native warehouse data delivery or data sharing patterns (e.g., Snowflake Data Sharing, Marketplace).

- Background in fintech, financial services, or a similarly data-intensive regulated industry.

- Exposure to agentic or LLM-based workflows and the data infrastructure that supports them.

- Experience with BI tooling (e.g., Looker, Tableau) and how semantic layer investments connect to the presentation layer.