Senior Analytics Engineer

Alloy is where you belong!

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 are creating a workplace where you can grow, thrive, and belong. See how we’ve been continuously recognized and named one of Inc. Magazine’s Best Workplaces, Forbes America’s Best Startup Employers, Best Fintech to Work for by American Banker, year after year.

Check out our investors and read more about us here.

О команде

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.

Alloy operates in a hybrid-work environment. We look to foster collaboration and community by having our local employees onsite three days a week.

Что предстоит делать

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.

Who we’re looking for

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.

Nice to Have's

• 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.

We're a lean team, so your impact will be felt immediately, and opportunities will grow as the company scales up. If this all sounds like a good fit for you, why not join us?…

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