Site Reliability Engineer, Big Data (Remote, International)

Pulsepoint; United Kingdom; Remote; GBP 82.000 - 119.000; Vollzeit

Zusammenfassung

PulsePoint is building a data platform to stream and store massive event data across on-prem and cloud. You will own architecture, capacity planning, and incident response for distributed systems at scale.

You will focus on Kafka and Ceph, drive reliability, and mentor engineers while operating across teams. This role offers fully remote work and exposure to large-scale infrastructure challenges.

Qualifikationen

• 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.

Aufgaben

• Own lifecycle from architecture through deployment to capacity planning and incident response.
• Build streaming and storage systems at PulsePoint.
• Define platform architecture and engineering standards across teams.
• Mentor engineers and drive technical decisions for reliability.

Kenntnisse

Distributed systems

Kafka

Ceph

Mentoring engineers

Technical decision making

Tools

Kubernetes

Terraform

Ansible

ArgoCD

Prometheus

Grafana

Jobbeschreibung

WebMD and its affiliates is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.

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.

Technology

• Apache Kafka for messaging layer
• Hadoop and Ceph as distributed storage layer
• SQL Server backup and recovery
• Terraform, Ansible, Puppet, ArgoCD for operational automation
• Prometheus, Grafana, Icinga and PagerDuty as observability layer
• Bare-metal servers and hybrid cloud/on-prem infrastructure

What matters: understand distributed systems, failure recovery, and operational patterns at scale. Apache Kafka and Ceph expertise is most valuable.

Who we're looking for

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

Nice-to-haves:

• Multi-region replication and disaster recovery
• Cost optimization at infrastructure scale
• Hybrid on-prem/cloud operations

Why this role

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

We try to keep the process focused and practical.

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