Platform Engineer I

Precisely is a global leader in data quality, data enrichment, and location intelligence. This Platform Engineer role focuses on building automation tools, internal platforms, and systems that improve the stability, reliability, and efficiency of the company's global SaaS platform while reducing operational toil through code.

* Design, develop, and maintain automation tools and platform capabilities using Go, Python, or similar languages to eliminate manual toil and build self-service components across infrastructure, observability, reliability, and security * Build and enhance internal tools, integrations, and workflows that improve operational efficiency and developer productivity within the SaaS platform * Develop and support automation frameworks for incident response, infrastructure testing, monitoring optimization, and compliance validation, including CI/CD pipeline enhancements * Help develop observability and monitoring solutions including setting up alerts, contributing to noise reduction systems, automated SLO management, and cost optimization * Partner with SaaS Engineering to deploy application and infrastructure updates using automation tools and participate in on-call duties for incident response

* Bachelor's degree in Computer Science, Engineering, or related field (or equivalent work experience) * 3-5 years of experience in software development, platform engineering, Site Reliability Engineering (SRE), or DevOps with hands-on production environment support * Strong technical expertise in Kubernetes, cloud platforms (AWS, Azure, or GCP), Go and Python development, Linux, Infrastructure as Code (Terraform, CloudFormation, Ansible), CI/CD, container technologies, APIs, microservices, distributed systems, and SaaS production operations * Familiarity with AI-assisted tools such as Microsoft Copilot, GitHub Copilot, Claude, Cursor, or similar tools for troubleshooting, root cause analysis, coding, and operational problem solving * Ability to leverage AI tools to improve infrastructure automation, scripting, documentation, test generation, and engineering productivity

* Experience contributing to automation projects, internal tools, or monitoring implementations such as Datadog, Prometheus, Grafana, or Backstage * Experience with Kubernetes operators, controllers, or orchestration concepts * FedRAMP, SOC2, or compliance framework exposure * Familiarity with LLM APIs and agentic AI workflows with interest in applying AI capabilities to platform engineering and DevOps