Analytics Engineer

Merge is the leading provider of agentic tools and customer-facing integrations for frontier LLMs, Fortune 500 organizations, and B2B SaaS companies.

Our platform offers two core products: Merge for Product, and Merge for Workforce. Merge for Product enables teams to ship AI features and integrations at massive scale. Merge for Workforce helps engineering teams govern AI access at the company level. Merge's enterprise-grade platform handles the entire integration lifecycle, from authentication and security to monitoring and maintenance. Thousands of companies trust Merge to accelerate product development, unblock sales, reduce customer churn, and save engineering resources—allowing them to focus on their core product.

We're hiring an Analytics Engineer to own the data foundation behind how Merge understands its products, customers, and business. An analyst answers a question and then works out how to repeat it. You'll build the systems that make sure no one answers it by hand again. You think in systems and code: models, pipelines, tests, failure modes, and freshness that teams can rely on. You'll work with Engineering and partner with Finance, RevOps, Product, and GTM to turn changing business concepts into precise definitions and trusted data products. If you want to build the models from first principles, this is the role.

What will you do:

- Own and evolve dbt models across our product data and other internal systems.

- Maintain shared dimensions, facts, transformations, and metrics as source systems and business requirements change.

- Partner with domain experts to define company metrics, reconcile competing definitions, and consolidate duplicated business logic into trusted, reusable models.

- Add tests for uniqueness, completeness, relationships, business rules, and important edge cases. Improve documentation, lineage, and discoverability.

- Partner with Engineering on source contracts, data quality, freshness, warehouse performance, and issues that start upstream.

What experience is required?

- Degree in data science, computer science, statistics, or a similar quantitative field.

- 4+ years in analytics engineering, data engineering, data science, business intelligence, or a related data role.

- Advanced SQL, production experience with dbt or similar, and strong dimensional modeling (grain, facts, dimensions).

- Strong engineering habits: version control, modular design, code review, automated testing, and documentation. Working proficiency in Python. You own the correctness of every model whether you wrote the code or an AI assistant did.

- Comfort defining and building net-new models, metrics, and processes where definitions are still taking shape.

- Clear communication with technical and non-technical partners, and sound prioritization across competing requests.

Nice to have

- Experience at a B2B SaaS or developer tools company.

- Background working with append-only history, event data, or change data capture.

- Hands on experience with data contracts, lineage, observability, or automated data-quality monitoring.

- Familiarity with LLM and AI infrastructure data, or a genuine interest in learning it fast.

Compensation

- The cash compensation range for this role is $160K-$200K

- Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications. In addition to cash compensation, all employees receive an equity compensation package.