Data Engineering Manager, GTM & Marketing

About the role As a Data Engineering Manager focused on GTM & Marketing, you will build and lead the data engineering team responsible for turning complex commercial data into the trusted foundations that power decision‑making across Anthropic's GTM and Marketing organizations. You will partner closely with stakeholders across Data Science, Revenue Operations, Marketing, and Finance to connect the needs of our customers to the dynamics of our pipelines — from new logo acquisition through activation, expansion, and retention — for a rapidly scaling, consumption‑based AI platform. In this role, you will balance hands‑on technical leadership with people management, setting the strategic vision for GTM and Marketing data foundations while developing and mentoring your team. You will oversee the transformation of CRM, marketing automation, billing, usage, and third‑party data into canonical datasets and insightful data marts — owning the metric definitions that Sales, Marketing, Finance, and leadership rely on to run the business. Key responsibilities Build and scale the GTM & Marketing Data Engineering team, including hiring and mentoring data engineers. Define and execute the strategic roadmap for GTM and Marketing data foundations and analytics capabilities. Oversee the design and implementation of scalable data pipelines, data models, and analytics solutions that transform CRM (Salesforce), marketing automation, billing, usage, and third‑party data sources into canonical datasets and data marts. Partner with Data Science, RevOps, Marketing, Finance, and leadership to understand data needs and translate them into technical requirements. Establish and maintain high data integrity standards, SLAs, alerting, and best practices for the team. Drive the development of foundational data products, dashboards, and tools to enable self‑serve analytics. Partner with Data Science to build innovative data tools using Claude to scale data‑driven decisions across GTM and Marketing teams. Foster a culture of technical excellence, continuous learning, and data‑driven decision‑making. Serve as a technical thought leader for data modeling, ETL processes, and GTM/Marketing analytics infrastructure. Minimum qualifications Experience managing data engineering teams in a high‑growth environment. Deep expertise in data modeling, ETL pipelines, and data warehouse architecture. Strong proficiency in SQL, Python, dbt, and modern data stack tools. Experience modeling GTM and Marketing data domains — including Salesforce/CRM, marketing automation platforms (e.g., Marketo, HubSpot), and billing/subscription/usage data. Demonstrated experience building and leading high‑performing data engineering teams. Track record of partnering with cross‑functional stakeholders (Sales, Marketing, RevOps, Finance) to deliver pipeline, revenue, attribution, and funnel metrics. Ability to balance strategic thinking with hands‑on technical leadership. Strong communication skills with the ability to translate complex technical concepts for non‑technical audiences, including executive stakeholders. Preferred qualifications 8+ years of total experience in data engineering or similar data‑focused roles, with 5+ years in a people management capacity. Prior experience in a scaling startup or B2B SaaS environment. Experience with multi‑segment, multi‑product B2B sales or commercial analytics, especially with consumption‑based revenue models and product‑led growth (PLG) motions. Experience with attribution modeling and account/lead/opportunity hierarchies. Track record of establishing data governance, quality standards, metric definitions, and best practices at scale. Experience scaling analytics functions from early stage to maturity in rapidly changing environments. Comfort operating in ambiguous, fast‑moving environments where you must create structure and drive forward progress. A passion for Anthropic's mission of building safe and beneficial AI. Compensation Annual Salary: $370,000 — $450,000 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! #J-18808-Ljbffr jobr.pro