Streaming Infra - Affirm (Replacement)

Role overview A contract engineering position supporting a large-scale Data and Storage Services organization that runs OLTP, OLAP, streaming, and batch workloads. The role focuses on operating and improving data infrastructure systems, ensuring reliability and observability, and partnering with internal teams that depend on these platforms. Strong Python skills and modern cloud infrastructure experience are central to the day-to-day work.

Responsibilities - Support and operate large-scale data infrastructure systems spanning OLTP, OLAP, streaming, and batch workloads. - Maintain and improve infrastructure reliability through automation, monitoring, and incident response. - Develop and enhance tooling and services in Python to streamline operational workflows and modernize the Python ecosystem. - Automate development workflows to improve communication, visibility, and efficiency across teams. - Respond to internal partner questions and escalations with clear, timely, and technically sound answers. - Create and maintain technical documentation, monitoring dashboards, runbooks, and onboarding materials to support platform users.

Requirements - Strong programming experience in at least one of Python or Java. - Hands-on infrastructure operations experience with AWS and Kubernetes. - Proven track record maintaining large-scale distributed systems in production. - Excellent communication skills with the ability to collaborate across engineering and product teams. - Demonstrated technical writing ability producing clear documentation, runbooks, and user guides.

Nice to have - Experience with streaming systems such as Flink or Kafka. - Familiarity with data platform technologies such as Spark, data lakes, MySQL, or Snowflake. - Knowledge of data processing frameworks and pipelines for near real-time streaming data. - Experience contributing to or managing shared infrastructure services used across multiple teams. - Experience leveraging LLMs and AI to automate and scale support workflows.