Description
Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.
As a Data Engineer on the GTM Systems data foundations team, you'll help build 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.
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