Senior SRE

Details

• Sees a recurring alert or a fragile deploy path and cannot leave it alone. Excels at shipping the right fix and the right automation, not the perfect one.
• Has run real production systems at scale — not just written runbooks for them.
• Has built with Datadog, OpenTelemetry, incident tooling, and AI coding assistants long enough to have strong opinions about what fits our needs.
• Can prototype an operational agent in Cursor and iterate as they go.
• Operates with autonomy, and can carry a technical discussion on system architecture, failure modes, and tradeoffs.
• Is genuinely curious about applying emerging AI to reliability and operations.
• Own the reliability roadmap end to end. Prove a repeatable define → emit → ingest → dashboard → alert metric pipeline, set SLOs and error budgets, prioritize the work, and drive execution. You'll partner with engineering on what we monitor, how, and when — indexing on user impact over low-level infrastructure.
• Take the financial data platform from functional to enterprise-grade, with a focus on availability, performance, and recoverability. Strengthen deployment paths, straight-through processing, and failover so the monthly close runs faster and cleaner as legacy hops are retired.
• Extend instrumentation across the six target systems — Velocity, Red Panda, MuleSoft, Snowflake, Fabric, and AWS (with D365 ledger to follow) — proving both push (OpenTelemetry) and pull (agent) ingestion. Cover service health (latency, error rates, throughput) and business KPIs (match rate, reconciliation completeness, settlement correctness and latency).
• Build the on-call, alerting, and blameless postmortem process that keeps reliability high as systems and the team grow. Route alerts Datadog → Incident.io with ServiceNow as the system of record, and set severity standards, escalation norms, and follow-up tracking that actually closes the loop.
• Build the tooling that automates routine operations, self-heals common failures, and surfaces signal over noise. Establish data lineage and retention, and validate reliability at scale — 5,000+ transactions before go-live — through auto-remediation, capacity planning, and actionable dashboards.
• Design and ship AI agents for incident triage, log analysis, and root-cause investigation (to name a few). Use Cursor as your build environment. Treat the agents as products solving specific problems.
• Partner with the AI platform team to deploy your agents on the org's AI fabric. Make them discoverable, governed, and reusable across functions.
• Proven experience designing, operating, and scaling reliable production systems.
• Deep hands-on expertise with modern observability tooling — Datadog, Prometheus/Grafana, and OpenTelemetry — including both push and pull ingestion patterns.
• Strong background defining SLIs, SLOs, and error budgets — and translating them into business-level KPIs, not just infrastructure metrics.
• Experience operating data platforms (Snowflake, Fabric) and enterprise integration layers (MuleSoft) alongside enterprise SaaS such as D365 (F&O and/or Power Apps).
• Hands-on incident management experience with tools like Incident.io and ServiceNow, and a track record of running effective on-call and postmortem practices.
• Hands-on experience building with LLMs and AI coding assistants — Cursor in particular. Bonus if you've built and deployed agents.
• Ability to define reliability strategy, reliability targets, and operational metrics — and defend them to engineering leadership and the business.
• Strong communication skills — you can explain a root cause to a junior engineer and a reliability risk to a product lead.
• Demonstrated bias for action and ability to operate autonomously in ambiguous, fast-changing environments.
• Experience in insurance, fintech, or other regulated financial services industries.
• Familiarity with insurance and finance concepts (premium, claims, settlement, reserving, monthly close) or willingness to learn them deeply.
• Experience with streaming and event pipelines (Red Panda / Kafka) and data lineage, retention, and auditability requirements.
• Strong working knowledge of chaos engineering, performance and load testing, and capacity planning.
• Experience deploying AI agents on an internal AI platform or fabric (governance, eval harnesses, prompt/version management).