Top Senior Linux Jobs in San Diego, CA

Senior product-minded engineer owning end-to-end SaaS product areas: discovery, design, implementation, testing, and iteration. Translate ambiguous goals into workflows and technical plans, make architecture decisions, build polished UX, use and validate AI-assisted development tools, communicate clearly, and drive work independently with cross-functional partners.

Lead day-to-day operations for multiple engineering teams, drive sprint planning and quarterly roadmaps, remove blockers, improve delivery processes, coach and develop engineers, review technical and architectural decisions, coordinate cross-team dependencies, partner with product and design, and track engineering metrics to continuously improve quality and throughput.

As a Senior Software Engineer, you'll design scalable data models, optimize performance, enhance a custom query engine, and develop data-driven features for complex customer needs.

The Senior Engineer I- Product will develop strategies for new product validation and qualification, collaborate with teams, resolve manufacturing issues, and improve product quality and profitability.

Lead a small engineering team to operate and evolve Nordstrom's data platforms (BigQuery, Looker, Metabase) across GCP and AWS. Own reliability, incident response, cost and access governance, Looker/LookML standards, conversational analytics enablement, and team hiring and development. Act as a player-coach: stay hands-on writing code, building automation, and driving technical direction and operational health.

Design and evolve scalable, resilient software solutions; gather requirements; produce and evaluate architecture artifacts; create and prioritize enabler features; guide development teams and align standards and processes.

Design and implement a scalable observability stack (logging, metrics, tracing, APM); select and configure agents; build alerting, incident management, runbooks, and dashboards; automate toil and codify monitoring as IaC; reduce MTTD/MTTR and train teams on instrumentation and incident response.

Build, maintain, and evolve high-performance, scalable data stores. Collaborate with platform and application teams to optimize storage solutions, implement best practices for data modeling and observability, automate operations, plan capacity, participate in architecture and code reviews, and mentor junior engineers to ensure reliability and cost-efficiency at scale.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.

Own and operate an AWS + Kubernetes (EKS) analytics platform for business consulting. Lead GitOps deployments and end-to-end GitLab CI/CD, manage incident response, observability, security baselines, access controls, and secrets. Balance reliability, cost, performance, and compliance while automating deployments across environments.