About the role
Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. Role Overview The Product Manager for SRE & Data Engineering is a strong technical product leader responsible for driving the lifecycle of internal Platform-as-a-Service (PaaS) and Data-as-a-Service (SaaS) products. You will oversee highly technical engineering teams, manage huge cloud budgets, ensure enterprise-grade system availability, and lead cross-departmental initiatives encompassing data platforms, AI automation, and security compliance. Key
Responsibilities Infrastructure & SRE Operations System Reliability: Define, monitor, and enforce error budgets to ensure 99.99% product availability. Scaling: Create and execute production system scaling strategies and lead cloud infrastructure operational maintenance (e.g., Google Kubernetes Engine (GKE) cluster consolidation). Cloud FinOps: Own the GCP cloud computing budget. Analyze pricing structures and identify process flows to improve productivity and reduce operational costs (e.g., achieving 25% cost reductions) Data Architecture & AI Initiatives Table & Pipeline Optimization: Work with Data Engineers to prioritize performance tuning- such as partitioning and clustering BigQuery tables or optimizing ETL/ELT pipelines—so business users get their data faster. Enabling AI/ML: Ensure the data architecture is structured to support advanced analytics and Machine Learning models, and prepare clean, accessible datasets for Data Scientists. Data Governance & Security: Partner with Legal and Security teams to ensure the AWS/GCP environment complies with privacy regulations (GDPR, CCPA) using Cloud IAM (Identity and Access Management) and strict data access policies. Required Skills &
