Wpromote

Staff Software Engineer, Platform & Solutions

Vaga remota de Platform Engineering com fit claro de localização do candidato.

Publicada18 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

CI/CD Docker GCP Kubernetes PostgreSQL Python Snowflake

Resumo da vaga

Staff Software Engineer, Platform & Solutions

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

You Will Be

Architecting and improving cloud foundations, especially on Google Cloud Platform, to support scalable, secure, well-governed workloads.
Designing and building platform capabilities across GCP, Kubernetes, serverless, CI/CD, GitOps, observability, AI, and developer tooling.
Establishing and improving GitOps workflows using tools such as GitHub Actions, Argo CD, Terraform, Helm, and automated release pipelines.
Managing and evolving enterprise Kubernetes clusters, including workload standards, deployment patterns, security controls, and operational practices.
Building reusable Terraform modules, Helm charts, templates, SDKs, scripts, and internal tools that reduce repeated manual work.
Improving developer experience by creating paved roads, self-service workflows, better documentation, standardized environments, and reliable delivery pipelines.
Implementing secure-by-default engineering patterns for IAM, Workload Identity Federation, secrets management, network security, auditability, and least-privilege access.
Helping software teams integrate with observability tooling such as OpenTelemetry, Prometheus, Grafana, Sentry, or similar systems.
Troubleshooting difficult production issues across applications, infrastructure, data workflows, cloud services, and deployment systems.
Partnering with application, data, product, and UX stakeholders to design practical solutions to complex business and technical problems.
Supporting data and AI platform needs where needed, including PostgreSQL, dbt, BigQuery, Snowflake, CDC, Data Lakes, and other data tools.
Comfortable maintaining both infrastructure and DAGs for an orchestration platform at scale, preferably Airflow.
Mentoring engineers and raising the technical bar through code review, architecture review, pairing, documentation, and direct implementation.
Evaluating vendors, frameworks, and platform tooling with a pragmatic build-versus-buy mindset.
Helping improve cloud cost visibility, resource governance, budget controls, and FinOps practices.

You Must Have

10+ years of professional software engineering experience.
Significant experience operating as a senior or staff-level engineer across multiple technical domains, not just one specialty.
Extensive, hands-on experience with cloud infrastructure, preferably on Google Cloud Platform, including Terraform, Kubernetes, Docker, cloud-native architecture, and production workload operations.
Strong experience designing and maintaining CI/CD pipelines using GitHub Actions, Argo CD, and GitOps principles to enable automated deployment workflows, environment management, and release automation.
Strong OO background and programming fundamentals, as well as scripting and automation skills using Python or Bash.
Experience with observability practices, including metrics, logs, traces, alerting, dashboards, SLOs, and incident response.
Ability to debug and improve systems across application code, infrastructure, data pipelines, networking, deployments, and cloud services.
Ability to quickly learn unfamiliar systems and make sound technical decisions with incomplete information.
Strong communication and leadership skills with engineers, product managers, and technical leadership.
A pragmatic problem-solving mindset; this role requires someone who can move quickly between systems, learn on the fly, and create durable solutions without over-engineering.

Nice to haves

Knowledge of GCP Enterprise Foundations Blueprint or similar enterprise cloud foundation patterns.
Clear understanding with GKE, Workload Identity Federation, Artifact Registry, Cloud Run, Cloud Functions, Pub/Sub, BigQuery, or related GCP services.
Examples of building internal developer platforms, self-service infrastructure, golden paths, or platform APIs.
Comfort working with data platform tools such as Airflow, dbt, BigQuery, Snowflake, Data Lakes, Kafka, Pub/Sub, and GCS.
Familiarity with serverless architectures and event-driven systems.
Awareness of SOC 2, PCI, CIS benchmarks, or similar security and compliance frameworks.
Experience modernizing legacy systems, monoliths, deployment processes, or poorly governed cloud environments.

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