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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Site Reliability / Platform Engineer - **Company:** Genesis10 - **Location:** Austin, TX, United States (Remote available) - **Salary:** $187,200.0 - $218,400.0 - **Contract:** Temporary contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Application Performance Management, Systems Engineering, Big Data, Cloud Computing, Databases, Data Infrastructure, DevOps, Intrusion Detection and Prevention, Python (Programming Language), Machine Learning, Operational Data Store, Reliability Engineering, Ansible, Software Engineering, Systems Integration, Enterprise Data Management, Datadog, Snowflake, Software Troubleshooting, Agentic-AI, Data Lakes, Kubernetes, Data Management, Terraform, AIOps, Service Stack, Databricks - **Published:** September 30, 2026 - **Apply:** https://www.dice.com/job-detail/e31ff6a4-338d-43a0-918b-c7d39c88bdb1 ## About the Role * Strong hands-on experience in software development, with some additional experience in SRE, DevOps, Platform Engineering, or Infrastructure Engineering * Strong Terraform experience, including the ability to write, deploy, maintain, and troubleshoot Infrastructure-as-Code * Strong Ansible experience, including developing playbooks/roles and automating software and infrastructure deployments * Hands-on Kubernetes experience, including deployment, configuration, troubleshooting, and production support * Strong understanding of observability, including metrics, logs, traces, monitoring, alerting, dashboards, and production troubleshooting * Experience with Datadog or a comparable enterprise observability platform; direct Datadog experience is strongly preferred * Experience automating the deployment and configuration of infrastructure and/or observability tooling * Strong coding or scripting capabilities; Python and/or Java experience is highly desirable * Experience working with cloud infrastructure and an understanding of how applications, networks, infrastructure, and data platforms interact * Strong troubleshooting and root-cause analysis skills across both application and infrastructure layers * Experience supporting highly available, production-scale systems. * Demonstrated ability to solve infrastructure and operational problems through code and automation rather than manual processes * Ability to work independently, learn unfamiliar technologies quickly, and take ownership of problems from investigation through implementation * Only candidates available and ready to work directly as Genesis10 employees will be considered for this position, * Experience building, consolidating, or modernizing enterprise observability platforms * Hands-on experience deploying or managing Datadog through Ansible, Terraform, or other automation * Experience with data lakes, Databricks, Snowflake, or other large-scale data platforms * Exposure to AIOps, AI/ML, agentic AI, automated remediation, or self-healing systems * Experience using Python or Java for infrastructure, platform, or operational automation * Experience working in a startup or similarly broad engineering environment where engineers have responsibility across multiple layers of the technology stack * Financial services experience is a plus but not required ## Description We are seeking a highly hands-on SRE / Platform Engineer for a hybrid software development, Site Reliability Engineering, and systems engineering role. This position is ideal for a strong developer who has expanded into infrastructure, cloud, automation, and production engineering and wants to take a more holistic view of how applications and systems operate together. The team follows an engineering-focused SRE model centered on using software and automation to solve infrastructure and operational problems. Engineers on the team write code every day and work across application and infrastructure layers to improve reliability, performance, scalability, observability, and system integration. A major initiative for the team is establishing a centralized observability capability across an environment where monitoring and operational data have historically been siloed. The organization is bringing telemetry together using Datadog and enterprise data lake capabilities, creating a common observability foundation that can ultimately support AIOps, agentic AI, automated remediation, and self-healing systems. This is not a traditional operations or Solutions Architecture position. The successful candidate will be expected to build, deploy, automate, troubleshoot, and improve the environment using technologies such as Kubernetes, Terraform, Ansible, and Datadog., * Develop software and automation to improve the reliability, scalability, performance, and operational efficiency of production systems * Write and maintain infrastructure and configuration code using Terraform and Ansible as part of day-to-day engineering activities. * Deploy, manage, automate, and troubleshoot applications and services running on Kubernetes * Automate the deployment and configuration of Datadog and other observability capabilities, including Ansible-based deployments * Help establish centralized observability across previously siloed applications, infrastructure, cloud, and data environments * Bring together metrics, logs, traces, events, and other telemetry to provide a comprehensive view of system health, dependencies, and potential business impact * Build monitoring, dashboards, alerting, and observability capabilities that enable teams to identify and resolve production issues more effectively * Help create the observability and operational-data foundation required to support future AIOps and agentic AI capabilities * Support longer-term initiatives around intelligent incident detection, root-cause analysis, impact analysis, automated remediation, and self-healing * Partner with application developers to improve application performance, resiliency, deployment patterns, and integration with other systems * Evaluate how applications interact with infrastructure, networks, APIs, databases, and data platforms and identify opportunities for improvement * Troubleshoot complex production issues spanning applications, Kubernetes, cloud infrastructure, networking, and data environments * Provide advanced production engineering support across traditional L2/L3 boundaries, focusing on root cause and permanent solutions rather than temporary fixes * Support large-scale data environments that may include Databricks, Snowflake, and enterprise data lake technologies * Identify repetitive or manual operational processes and replace them with scalable, code-based solutions