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
$270,000—$345,000 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.
For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
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.
