Data Engineer

About the role

Anthropic's go-to-market teams are scaling quickly, and the decisions they make, from who to sell to and how to cover them to where to invest next, are only as good as the intelligence and infrastructure underneath them. As a Data Engineer on the GTM Systems data foundations team, you'll help build that layer: the data products that give sellers, operators, and analysts a rich, current, and trusted picture of the market, and the engineering rigor that lets our GTM systems run reliably as the business grows.

This is an engineering role that goes beyond pipelines. You'll treat GTM data as a product and a platform: built once, kept current, and available wherever people work. Alongside the build, you'll help shape what the data means: how attributes are defined, which sources are trusted, and how the business puts them to use. The role sits at the intersection of the sales organization, the data organization, and the systems that data flows through. You'll partner closely with Sales Strategy and Operations, Finance, Data Science, and the central data engineering team.

Key responsibilities

Bring disparate internal and external sources together: orchestrate, model, and normalize first-party product and revenue data alongside third-party data into one usable understanding of the company landscape

Build and maintain identity and entity resolution, so every source attaches to one trusted record for each company and the relationships between companies are clear

Help build the canonical layer where key attributes are defined once and mean the same thing everywhere, with clear sourcing logic, provenance, and freshness

Turn data into intelligence: scores, signals, and AI-generated attributes that help GTM teams prioritize and act, held to the same quality bar as any other source

Deliver that intelligence to the surfaces where teams work, including the CRM, and bring production discipline to how data moves through those systems

Operate what you build as a living system, with observability, data quality, cost, security, and access controls designed in, so problems surface before the field sees them

Partner with the owners of GTM processes such as planning, segmentation, and routing to settle definitions, make clear commitments, and communicate tradeoffs

Make the platform easy for other teams to build on, so work is contributed once and not rebuilt in silos

Minimum qualifications

Experience as a data engineer or analytics engineer building and operating production pipelines and data models

Strong SQL and Python, and experience with modern transformation and orchestration tooling on a cloud data warehouse

Experience making messy data from many sources usable, including record matching, deduplication, and reconciling conflicting values

Familiarity with go-to-market data and the CRM systems it flows through

Judgment beyond the pipeline: weighing data sources, shaping definitions with business partners, and making calls on data quality

Clear written and verbal communication with technical and non-technical partners

Regular hands-on use of AI tools in your engineering work

Preferred qualifications

5+ years of experience in data engineering, analytics engineering, or GTM data roles

Experience building a shared data product or platform that several teams depend on and contribute to

Experience with company data such as firmographics, corporate hierarchies, or master data management

Experience using LLMs inside data pipelines and evaluating their output

Hands-on Salesforce experience, including working at large data volumes

Experience supporting GTM planning processes such as territory design, segmentation, or scoring

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$320,000—$405,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.