Tech Lead - Real-Time Fraud Platform Architect

DataVisor; Mountain View (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

DataVisor's Platform Engineering team is building the next-generation machine learning platform that powers real-time fraud detection at scale. You will own the architecture for streaming, storage, and training pipelines that support core detection capabilities.

We are seeking a hands-on engineering leader who mentors engineers, drives architectural decisions, and maintains high standards for reliability and operational excellence across distributed systems in production.

Qualifications

• Bachelor's degree in Computer Science or a related field is required.
• 8+ years of software development experience with production systems.
• 2+ years of technical leadership experience (tech lead, staff engineer, engineering manager).
• Deep production experience with Java and working proficiency in Python and Shell scripting.

Responsibilities

• Own the technical direction of the real-time detection platform (streaming, storage, training pipelines).
• Translate product/engineering roadmaps into concrete technical plans and milestones.
• Identify risks, dependencies, and trade-offs early in delivery.

Skills

Technical leadership

Mentoring engineers

Ownership & accountability

System design & architecture

Code quality & reliability

Education

Bachelor's degree in Computer Science or related field

Tools

Java

Python

Shell scripting

Cassandra

Yugabyte

Flink

Spark

Kafka

Spring Framework

AI coding tools (Claude Code, Cursor, GitHub Copilot)

Job description

DataVisor's Platform Engineering team is building the next-generation machine learning platform that powers real-time fraud detection at scale. You will own the architecture for streaming, storage, and training pipelines that support core detection capabilities.

We are seeking a hands-on engineering leader who mentors engineers, drives architectural decisions, and maintains high standards for reliability and operational excellence across distributed systems in production.