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