Technical Solutions Specialist, Data Operations
AnthropicSan Francisco, CA | New York City, NYPosted 18 March 2026
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.
Technical Solutions Specialist, Data Operations
Strategic Data Partnerships | Anthropic
About the Role
Working in Data Operations on the Strategic Data Partnerships team at Anthropic, you will support a cross-functional team in implementing partnership strategies to improve Anthropic’s products. You’ll ensure data meets our standards and reaches the right teams, build systems to track compliance and data usage across the portfolio, and coordinate across Research, Product, Legal, and external partners to remove barriers and accelerate impact. This role requires operational excellence combined with technical hands-on execution, and is a great fit for someone who wants to apply those skills in a high-impact, fast-growth context.
You’ll serve as the technical engine of the team’s data operations—building and maintaining the systems, programs, and data pipelines that help us evaluate, filter, and deduplicate large volumes of partner data, and managing the cloud infrastructure that supports data delivery. This role sits at the bridge between external partners delivering data and internal research teams consuming it: you’ll resolve technical issues on both sides while proactively developing tools, documentation, automation, and workflows to scale the operation.
Responsibilities
Data Opportunity Assessment and Processing
Analyze and review incoming or prospective data to verify it is useful and strategic for Anthropic
Own and maintain Python-based ETL pipelines that process large partner datasets, applying filtering criteria and deduplicating against existing data
Write and optimize SQL queries against large relational databases to support filtering and analysis workflows
Refine processing logic as requirements evolve across new data types and formats
Data Delivery Infrastructure, Tooling, and Support
Own end-to-end data delivery workflows, ensuring data moves seamlessly from partners to internal teams to accelerate time-to-impact
Manage AWS and GCP resources for receiving and organizing partner data deliveries
Troubleshoot delivery issues and coordinate with partners on formatting and transfer protocols and resolve technical escalations from partners and internal teams
Build and maintain internal systems, scripts, and automation that support the team’s workflows
Support occasional research evaluation tasks as needed
Data Operations and Governance
Develop and maintain Anthropic's preferred standards for receiving, consuming and cataloging data, ensuring alignment with Product and Engineering's evolving needs
Contribute to systems for monitoring data usage and compliance with partner agreements
Partner with teammates and cross-functional stakeholders to build out governance practices as the team scales
You May Be a Good Fit If You
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience
5-7+ years of experience with data pipelines or data engineering workflows
Background in solutions engineering, partner engineering or related role at a large tech company
5+ years of experience in technical troubleshooting or writing code in one or more programming languages
Proficiency in Python and SQL, including writing, debugging, and optimizing scripts and queries against large datasets
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure), including managing storage, configuring access, and working from the CLI
Excellent problem-solving skills with a track record of debugging technical issues, whether at the code level or within a broader system
Some experience interacting with external third parties delivering data
Strong C ... (truncated, view full listing at source)
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