Pay
Higher than most similar jobs
$320K–$405K against $127K–$190K, the middle half of what similar US postings state.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Data Engineer
San Francisco, CA | New York City, NY; Seattle, WA
Actively hiring, first seen by us 4 days ago- Pay
USD 320,000–405,000/year — pay source
Annual Salary: $320,000—$405,000 USD Logistics
The annual compensation range for this role is listed below.
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
Before you apply
- Work authorization
- Sponsors visas
What you’ll work on
Full postingAnthropic'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.
This is an engineering role that goes beyond pipelines.
Combine first-party product and revenue data with third-party sources into one company view
Build identity and entity resolution so each company maps to one trusted record
Help define a canonical layer of key attributes with clear sourcing and freshness
Deliver scores, signals and AI-generated attributes to CRM and other work surfaces
Operate pipelines with observability, data quality, cost and access controls built in
From the employer’s posting
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.
orchestrate, model, and normalize first-party product and revenue data alongside third-party data
Build and maintain identity and entity resolution, so every source attaches to one trusted record for each company
Help build the canonical layer where key attributes are defined once and mean the same thing everywhere
Deliver that intelligence to the surfaces where teams work, including the CRM
Operate what you build as a living system, with observability, data quality, cost, security, and access controls designed in
What you’ll bring
All qualificationsCore experience
- 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
Preferred experience
- 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
Qualification wording
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
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
Education & alternatives
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
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 31, 20264 days ago
- Recorded sightings
- 5
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 4
Early. Similar jobs usually come down between day 5 and day 54.
From 10,704 similar postings that came down. Coming down is not always a hire.
Anthropic is hiring
27 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Engineer hiring is picking up
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
Where the posting names them
- 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
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
About Anthropic
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.
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.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Annual Salary: $320,000—$405,000 USD Logistics
More source context
The annual compensation range for this role is listed below.
- Location & working pattern
San Francisco, CA | New York City, NY; Seattle, WA
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.
- Work authorization
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.