Senior Analytics Engineer

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.

The Opportunity: We are looking for a founding-level Senior Analytics Engineer to own the data stack from the gold layer onward. Our Data Engineering team handles raw ingestion, warehouse reliability, and the bronze-to-silver layers. This role picks up from there to ensure the organization has trusted, consistent, and reusable definitions for key business performance metrics. For example, operational performance and transaction throughput metrics should mean the same thing whether queried in a BI dashboard, surfaced through an internal Slack chatbot, or consumed by an LLM-based agent. 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 OwnOwn 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 Experiencedbt 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 different interpretations of the same metric and create a clear, trusted source of truth. You should be comfortable asking questions such as: What does this metric actually mean? Who should own its definition? Why are two teams calculating it differently? Which version should become the company standard? How should that definition be represented in dbt, BI tools, and future AI applications? The strongest person for this role combines technical depth, business curiosity, and strong cross-functional judgment.

Green Flags: Founding or first analytics-engineering ownership at a startup. Direct ownership of semantic or metrics infrastructure. Strong Snowflake + dbt experience. Clear examples of creating metrics or data contracts used by multiple teams. Experience resolving disagreements between Product, Finance, Operations, or other business teams around metric definitions. Strong documentation habits and writing-first communication. Initiative beyond the formal job description. Experience thinking about how analytics models support both BI tools and AI agents. Experience with AI, LLMs, or agentic data consumers. What May Not Be a FitData engineering infrastructure, orchestration, or ingestion with limited analytics modeling. Pipeline development without meaningful SQL/dbt ownership. Analytics roles without ownership of shared metrics or semantic layers. Highly academic or research-heavy backgrounds with limited production experience. Candidates who have mainly contributed to models owned by a centralized Data Engineering team rather than leading analytics-layer ownership themselves. Location & Compensation

Location: New York City, NY — Hybrid.

Compensation: $180K–$210K base salary + equity. Relocation and visa sponsorship may be considered on a case-by-case basis. Why This Role Is Interesting This is an opportunity to become the person who defines how the company understands and uses its own data. Rather than inheriting a fully mature analytics function, you will have the chance to shape the company’s semantic layer, establish metric governance, influence how teams make decisions, and build data products that will eventually serve both people and AI systems.