Data Engineer

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. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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.

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  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

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  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

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

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  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

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

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  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

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

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

• 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

• 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

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.

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.

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.

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.