Data Engineer - GTM Systems Data Foundations NEW

The Briefed; San Francisco (CA); Hybrid; USD 320,000 - 405,000; Full time

Benefits offered by this job

Salary + equity package

Health, vision, dental

Flexible PTO

Visa sponsorship

Job summary

Anthropic is seeking a Data Engineer on the GTM Systems Data Foundations team. You will treat go-to-market data as a product and build pipelines, MDM layers, and AI-driven signals to deliver trusted market insights to sales surfaces like Salesforce.

You will work at the intersection of Sales Strategy, Finance, and Central Data Engineering, delivering scalable data models and production-grade data infrastructure in a hybrid work setting with on-site time in San Francisco.

Qualifications

• Bachelor’s degree or equivalent in computer science, data engineering, information systems, or related field.
• 5+ years of professional data/analytics engineering experience.
• Experience building production data pipelines and data models at scale.
• Ability to meet hybrid work policy (25% onsite).

Responsibilities

• Ingest, normalize, and model multi-source first-party usage/revenue data with third-party sources.
• Build master data management and identity resolution systems.
• Create canonical data layers with provenance, auditability, and security controls.
• Integrate AI-generated signals into data pipelines and sales intelligence.
• Coordinate data movement between central warehouses and CRM systems like Salesforce.
• Collaborate with Sales Ops, Finance, and Data Science on GTM planning.
• Develop extensible, self-serve data models for use by other teams.

Skills

Data Engineer

Analytics Engineer

Python

SQL

Go-to-Market data architectures

CRM platforms

Hands-On AI Usage

Education

Bachelor's degree

Tools

dbt

Airflow

Dagster

Snowflake

BigQuery

Databricks

Job description

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that benefit society. As our go-to-market teams scale rapidly, the operational decisions made across sales, territory planning, and account coverage rely directly on the infrastructure and data products built beneath them.

As a Data Engineer on the GTM Systems Data Foundations team, you will treat go-to-market data as both a product and a platform. Sitting at the intersection of Sales Strategy, Finance, Data Science, and Central Data Engineering, you will build data pipelines and canonical data models that unify first-party product usage and revenue data with third-party firmographic sources. You will architect master data management (MDM) layers, identity and entity resolution systems, and AI-driven sales intelligence signals, delivering trusted, current market insights directly to sales surfaces like Salesforce.

Key Responsibilities

• Multi-Source Data Orchestration: Ingest, normalize, and model disparate first-party usage/revenue data and third-party firmographic sources into a unified cloud data warehouse landscape.
• Identity & Entity Resolution: Build and maintain master data management systems that perform deduplication, record matching, corporate hierarchy mapping, and cross-source record linking.
• Canonical Layer Architecture: Establish single-source-of-truth business attributes with clear provenance, auditability, freshness guarantees, and strict security controls.
• AI-Generated Sales Intelligence: Integrate Large Language Models (LLMs) into data pipelines to generate predictive scores, buyer signals, and account attributes held to high production quality standards.
• CRM & Production Integration: Streamline data movement between central warehouses and field surfaces (e.g., Salesforce), maintaining production discipline, observability, and cost controls.
• Process & Stakeholder Alignment: Partner with Sales Ops, Finance, and Data Science leads to settle attribute definitions and support GTM planning (territory design, segmentation, lead routing).
• Platform Development: Build extensible, self-serve data models and utilities that prevent internal silos and enable other engineering teams to build on top of the foundation.

Core Technical & Engineering Skills

• Proven background as a Data Engineer or Analytics Engineer building, monitoring, and operating production data pipelines and data models at scale.
• High proficiency in Python and SQL, along with Hands-On experience using modern transformation and orchestration tools (e.g., dbt, Airflow, Dagster) on cloud data warehouses (Snowflake, BigQuery, Databricks).
• Demonstrated experience resolving messy, unstructured datasets—including record matching, entity deduplication, and reconciling conflicting multi-source attributes.
• Practical familiarity with Go-to-Market data architectures and Customer Relationship Management (CRM) platforms.
• Hands-On AI Usage: Regular, active integration of AI software development tools into your daily engineering workflow.

Education & Work Logistics

• Bachelor’s degree (or equivalent professional experience/training) in Computer Science, Data Engineering, Information Systems, or a related field.
• Ability to meet Anthropic's hybrid work policy (at least 25% time onsite in office).

Preferred Qualifications

• 5+ years of professional experience in data engineering, analytics engineering, or dedicated GTM data infrastructure roles.
• Prior experience building a shared, central data product or platform relied upon by multiple cross-functional teams.
• Domain expertise handling firmographics, corporate hierarchies, and Master Data Management (MDM) frameworks.
• Experience embedding LLMs directly inside data pipelines and constructing output evaluation benchmarks.
• Hands-On Salesforce data engineering experience, specifically operating at high data volumes.
• Practical experience supporting GTM operational processes such as territory planning, lead scoring, or account segmentation.

Total Rewards & Benefits

• Compensation & Equity: Base salary of $320,000 – $405,000 USD + competitive equity package with optional equity donation matching.
• Healthcare & Family Support: Comprehensive health, vision, and dental coverage; generous paid parental leave.
• Time Off & Flexibility: Flexible working hours and generous PTO policy.
• Workplace & Perks: Office space in San Francisco, collaborative research environment, and full visa sponsorship support through external immigration counsel.