Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 4
Go @ 7
Kubernetes @ 4
Terraform @ 4
Python @ 7
SQL @ 4
Spark @ 4
GCP @ 4
dbt @ 4
ETL @ 4
Java @ 7
Airflow @ 4
Cloud Composer @ 4
Distributed Systems @ 8
AWS @ 4
Communication @ 4
ELT @ 4
GDPR @ 4
FinTech @ 4
Reporting @ 4
Fivetran @ 4
OLAP @ 4
Audit @ 4
Compliance @ 4
AI @ 4
Data Pipelines @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. Data Infrastructure designs, operates, and scales secure, privacy-respecting systems that power data-driven decisions across Anthropic. The team provides data processing, storage, and access that are trusted, fast, and easy to use. This role sits at the intersection of data systems, security, and scalability and involves working directly with data scientists, analysts, and business stakeholders while diving deep into cloud infrastructure primitives.
Responsibilities
Within Data Infrastructure, you may be matched to critical business areas including:
- Data Governance & Access Control: design and implement robust access control systems ensuring only authorized users can access sensitive data. Build infrastructure for permission management, audit logging, and compliance. Work on IAM policies, ACLs, and security controls that scale across thousands of users and systems.
- Financial Data Infrastructure: build and maintain data pipelines and warehouses powering business-critical reporting. Ensure data integrity, accuracy, and availability for complex financial systems, including third-party revenue ingestion pipelines. Own reliability of systems processing revenue, usage, and business metrics.
- Cloud Storage & Reliability: architect disaster recovery, backup, and replication systems for petabyte-scale data. Ensure high availability and durability of data stored in cloud object storage (GCS, S3). Build systems that protect against data loss and enable rapid recovery.
- Data Platform & Tooling: scale data processing infrastructure using BigQuery, BigTable, Airflow, dbt, and Spark. Optimize query performance, manage costs, and enable self-service analytics across the organization.
Requirements
- 10+ years (not including internships or co-ops) of experience in a Software Engineer role, building data infrastructure, storage systems, or related distributed systems.
- 3+ years (not including internships or co-ops) leading large-scale, complex projects or teams as an engineer or tech lead.
- Ability to set technical direction for a team, not just execute within it.
- Deep experience with at least one of the below and strong proficiency in programming languages like Python, Go, Java, or similar.
- Experience with infrastructure-as-code (Terraform, Pulumi) and cloud platforms (GCP, AWS).
- Ability to navigate tradeoffs between performance, cost, security, and maintainability.
- Strong collaboration skills; ability to work with technical and non-technical stakeholders.
Preferred / Nice-to-have
- Experience with security and compliance requirements (ITGC, GDPR, financial controls).
- Background in data warehousing, ETL/ELT pipelines, or analytics infrastructure.
- Experience with Kubernetes, containerization, and cloud-native architectures.
- Track record of improving data reliability, availability, or cost efficiency at scale.
- Knowledge of column-oriented databases, OLAP systems, or big data processing frameworks.
- Experience working in fintech, financial services, or highly regulated environments.
- Security engineering background focused on data protection and access controls.
Technologies We Use
- Data: BigQuery, BigTable, Airflow, Cloud Composer, dbt, Spark, Segment, Fivetran
- Storage: GCS, S3
- Infrastructure: Terraform, Kubernetes, GCP, AWS
- Languages: Python, Go, SQL
Compensation
Annual Salary: $405,000 - $485,000 USD
Logistics
- Education: at least a Bachelor's degree in a related field or equivalent experience.
- Location-based hybrid policy: currently expect all staff to be in one of the offices at least 25% of the time; some roles may require more in-office time.
- Deadline: applications reviewed on a rolling basis.
Visa & Relocation
- Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist, though not every role/candidate is guaranteed successful sponsorship.
How we're different
Anthropic works as a single cohesive team on a few large-scale research efforts. The company values collaboration, communication, and impact on AI safety and reliability. Candidates are encouraged to read Anthropic research and apply even if they do not meet every listed qualification.