About this role
About the Role The Data Platform team within Alloy’s Intelligence vertical builds and maintains the infrastructure that shapes how data is modeled, governed, and delivered to customers. We combine data engineering rigor with analytical depth, leveraging dbt, Snowflake, and Artie, with growing investment in a semantic layer. This is an early, high-leverage moment for Analytics Engineering at Alloy, where you’ll help set patterns, standards, and culture for the function as it scales.
What You'll Do
- Serve as technical anchor for how Alloy models, governs, and exposes data, collaborating with Data Science, Product, Engineering, and client-facing teams to ensure data assets are trustworthy and scalable.
- 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 to support internal users and 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 observability and governance frameworks that keep data trustworthy at scale.
- Influence how Analytics Engineering is practiced at Alloy—a greenfield opportunity to set the standard.
What We're Looking For
- 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.
- Ability to partner effectively with Data Science, Engineering, and Product, translating 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 Alloy values: be bold, get scrappy, collaborate, and celebrate our differences.
Nice to Have
- 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 (Looker, Tableau) and how semantic layer investments connect to the presentation layer.
Compensation & Benefits
- Salary range: not disclosed in posting. Alloy is committed to fair and equitable compensation practices.
- Hybrid work arrangement: local employees onsite three days a week (Tue-Thu) at our Union Square HQ in New York City.