AI-Driven Kafka Platform Engineer - Onsite Austin

Procom

Austin (TX)

On-site

USD 140,000 - 190,000

Full time

3 days ago
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Job summary

Schwab is seeking a Kafka Platform Engineer for an onsite assignment in Austin, TX. You will bring 10+ years in distributed systems and deep expertise with the Confluent Kafka Platform across on‑prem and cloud environments.

In this role you will design secure Kafka architectures, drive HA/DR, assist with data replication, and lead AI-enabled development and automation initiatives to improve engineering efficiency at scale.

Qualifications

  • 10+ years of experience in distributed systems, messaging, and enterprise platform engineering.
  • Extensive hands-on experience with the Confluent Kafka Platform (on-prem and cloud).
  • Strong background in Linux/Unix, networking, OS performance and security.
  • Experience with automation, DevOps and operational tooling.
  • Experience leveraging AI-powered tools to accelerate development and productivity.

Responsibilities

  • Design and implement secure Kafka architectures with authentication, authorization, encryption and network isolation.
  • Define and support high availability, disaster recovery and data replication strategies.
  • Advise on data ingestion, event streaming and batch/stream processing architectures.
  • Design and build AI agents, or equivalents, to automate engineering workflows at scale.
  • Drive automation-first operations and integrate Kafka with CI/CD tooling.
  • Lead deep-dive investigations into production issues and incidents.

Skills

Distributed systems
Kafka Platform
Automation
DevOps
AI tools
Communication
Leadership

Tools

Confluent Kafka Platform
GitHub Copilot

Job description

Schwab is seeking a Kafka Platform Engineer for an onsite assignment in Austin, TX. You will bring 10+ years in distributed systems and deep expertise with the Confluent Kafka Platform across on‑prem and cloud environments.

In this role you will design secure Kafka architectures, drive HA/DR, assist with data replication, and lead AI-enabled development and automation initiatives to improve engineering efficiency at scale.

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