Senior Analytics Engineer

Location: New York

Senior Analytics Engineer for a Series B Venture backed Startup | New York City, NY | Hybrid Compensation: $180K-$211.5K base + equity

About the Company Our client is a fast-growing technology company building modern digital infrastructure for payments, transactions, and business operations.

As the company continues to scale its data and analytics capabilities, they are hiring a Senior Analytics Engineer to take foundational ownership of how core business metrics are defined, modeled, governed, and used across the organization. This is a highly visible role at the intersection of analytics engineering, data governance, business strategy, and cross‑functional decision‑making.

This is much more than a traditional transformation‑pipeline role. The Senior Analytics Engineer will own semantic layers, metric definitions, data contracts, cross‑team metric standardization, gold-layer modeling, certified data products, analytics cost management, and data context for both human and AI consumers.

What You'll Own

• Own the gold layer and ensure core business metrics are modeled consistently and reliably.
• Design and maintain the company’s semantic layer / metrics layer.
• Define metrics and data contracts that are adopted across multiple teams.
• Partner with stakeholders across the business to resolve disagreements around metric definitions.
• Establish trusted, reusable data products that can support dashboards, reporting, self‑service analytics, and AI/LLM-based consumers.
• Partner on the company’s broader reporting and analytics revamp.
• Expand data capture and improve the quality and usability of business‑critical datasets.
• Help manage and optimize analytics infrastructure costs, including Snowflake and dbt usage.
• Improve documentation and create a more scalable, writing‑first analytics culture.
• Work closely with Data Engineering while maintaining clear ownership of analytics‑layer modeling and governance.

What We're Looking For

• 5+ years of experience in analytics engineering, data analytics, or a closely related field.
• Strong, clearly demonstrated advanced SQL experience.
• Strong experience with dbt and Snowflake.
• Direct ownership of a semantic layer, metrics layer, or equivalent modeling framework.
• Experience defining metrics or data contracts adopted by multiple teams.
• Experience acting as the sole or lead analytics engineer for important modeling or metric work.
• Track record of resolving cross‑functional disagreements around how business metrics should be defined.
• Strong written and verbal communication skills.
• Ability to operate independently in a decentralized, fast‑moving environment.

Particularly Relevant Experience

• dbt Semantic Layer, LookML / Looker, Cube, Sigma, self‑service analytics tooling, semantic modeling, metrics governance, or data contracts.
• Snowflake or dbt cost optimization.
• Airbyte or Fivetran.
• LLM or AI‑enabled data products, structured chatbot context, MCP‑style interfaces, or data products used by AI agents.
• Client‑facing analytics or consulting work.
• A master’s degree in a relevant discipline is a plus, but not required.

Ideal Background

• The strongest candidates may have worked as Senior Analytics Engineer, Lead Analytics Engineer, Staff Analytics Engineer, Founding Analytics Engineer, Analytics Engineering Manager interested in returning to an IC role, or Senior Data Analyst with significant analytics‑engineering and modeling ownership.
• Strong candidates often come from high‑growth startups or high‑caliber technology companies that use a modern data stack (e.g., payments platforms, business software, SaaS, or high‑growth tech unicorns).

What Success Looks Like The ideal candidate is not simply someone who builds data models. They are someone who can step into a company where different teams may have…