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Datadog's Chaos Engineering team builds systems that surface reliability weaknesses before they become outages. As a Senior Software Engineer, you will initially focus on zonal resilience, building automation that helps services safely evacuate and recover from zonal failures, while also contributing to fault injection, incident replay, gameday orchestration, and reliability tooling.
You will work across engineering teams to design systems that safely exercise production failure modes and turn findings into verified remediation. You will also help advance the use of AI and automation to identify, test, and close resilience gaps as Datadog's software and infrastructure evolve.
Key responsibilities include:
- Build zonal-resilience automation that coordinates safe workload evacuations, switchovers, and recovery in partnership with the teams that own affected services.
- Design and build fault-injection systems for production environments, including infrastructure- and application-level testing, incident replay, and controlled resilience experiments.
- Develop safeguards such as blast-radius controls, kill switches, validation mechanisms, and rollback paths that keep production experiments contained and reversible.
- Build agents and automation that help propose failure scenarios, triage experiment results, and connect reliability findings to tracked remediation and verification.
- Lead gamedays from hypothesis and scenario design through execution, documented findings, remediation tracking, and validation of completed fixes.
- Design and implement reliable distributed systems, including gRPC services, Kubernetes controllers, and shared platform components, while contributing to technical design and mentoring other engineers.
Datadog operates as a hybrid workplace to ensure employees can create a work-life harmony that best fits them. You will develop deep expertise in distributed systems, production resilience, Kubernetes, and large-scale infrastructure, while collaborating with engineers across infrastructure, databases, observability, and service teams on complex systems challenges.
QUALIFICATIONS:
- Strong distributed systems fundamentals and ability to reason about consistency, failure modes, backpressure, idempotency, quorum, retries, and failure recovery.
- Understanding of Kubernetes workload lifecycles, including how pods, controllers, scheduling, draining, and eviction interact with resilient system design.
- Experience designing, building, or operating production systems where safety, availability, and controlled failure handling are important.
- Ability to communicate complex technical decisions clearly through design documents, runbooks, postmortems, and cross-functional technical discussions.
- Comfortable collaborating across engineering teams to understand unfamiliar systems, identify failure modes, and drive resilience improvements.
- Experience with reliability engineering, chaos engineering, zonal failover, AI-assisted operational workflows, traffic interception, or large-scale observability systems is beneficial but not required.
