[Hiring] Site Reliability Engineer, Big Data @PulsePoint

Role Description

Build streaming and storage systems at PulsePoint. PulsePoint processes billions of events daily through Kafka, Hadoop, HDFS, and Ceph. Our Data Platform team maintains these systems across hybrid infrastructure: bare-metal on-prem, cloud, and the integration between them. You own the lifecycle from architecture through deployment to capacity planning and incident response.

What you'll work on:

• Kafka architecture, topic design, governance, partition strategy, throughput and latency optimization.
• Ceph operations, pool design, placement optimization, capacity planning.
• Operational automation, reduce manual work, faster incident response, preventive systems.
• SQL Server backup and recovery pipelines, basic cluster support.
• Data team tooling with self-service capabilities and observability.

Qualifications

• You've operated data infrastructure at petabyte scale or billions of events/day.
• You understand replication failures, consistency tradeoffs, and cost-optimization of large systems.
• You automate before troubleshooting.
• You take ownership across system layers, not just one component.
• You simplify complex systems.

Requirements

• 5+ years operating distributed systems at scale in production.
• Deep expertise in Kafka, Ceph, or similar distributed infrastructure.
• Proven ability to design for scale and reliability.
• Experience mentoring engineers and making technical decisions.
• Willingness to work 9am-6pm ET U.S. hours.

Benefits

• This is not a ticket-driven operational role.
• You'll help define platform architecture, influence engineering standards and work on infrastructure that supports multiple engineering organizations.
• The engineer joining this role is expected to become a key technical contributor shaping the future of the platform.
• You can work fully remotely and get the opportunity to work on large-scale infrastructure and distributed systems challenges.

Hiring Process

• Introductory conversation (~60m): Learn about your background and discuss the role.
• Technical discussion (~60m): Deep dive into systems engineering, Kubernetes and operational experience.
• Architecture discussion (~60m): Explore platform design, distributed systems and technical decision-making.
• Leadership conversation (~30m): Meet engineering leadership and discuss team, strategy and long-term direction.