Staff+ Software Engineer, Data Infrastructure en Anthropic

Híbrido - San Francisco, CA | Seattle, WA

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The role focuses on designing, operating, and scaling secure, privacy‑respecting data infrastructure for Anthropic. Candidates will work at the intersection of data systems, security, and scalability, building pipelines, access control, cloud storage reliability, and data platform tooling to support business‑critical reporting and analytics.

Salary

USD 405,000 - 485,000

Requirements

Skills

  • 10+ years of experience in a Software Engineer role, building data infrastructure, storage systems, or related distributed systems
  • 3+ years of experience 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
  • 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 complex technical tradeoffs between performance, cost, security, and maintainability
  • Excellent collaboration skills
  • 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 with focus on data protection and access controls

Responsibilities

  • Design and implement robust access control systems ensuring only authorized users can access sensitive data, build infrastructure for permission management, audit logging, and compliance requirements, and work on IAM policies, ACLs, and security controls that scale across thousands of users and systems
  • Build and maintain data pipelines and warehouses powering business‑critical reporting, ensure data integrity, accuracy, and availability for complex financial systems, manage external relationships for upstream dependencies, and own reliability of systems processing revenue, usage, and business metrics
  • 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), and protect against data loss while enabling rapid recovery
  • Scale data processing infrastructure using technologies like BigQuery, BigTable, Airflow, dbt, and Spark, optimize query performance, manage costs, and enable self‑service analytics across the organization

Technologies

BigQueryBigTableAirflowCloud ComposerdbtSparkSegmentFivetranGCSS3TerraformKubernetesGCPAWSPythonGoSQL

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