> Markdown version of [/jobs/ext/2645533-senior-site-reliability-engineer-workflow-automation](https://www.wearedevelopers.com/jobs/ext/2645533-senior-site-reliability-engineer-workflow-automation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Senior Site Reliability Engineer - Workflow Automation

- **Company:** Dimensional Fund Advisors
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Adobe InDesign, Airflow, Amazon Web Services, Configuration Management, Information Engineering, Data Infrastructure, Linux, DevOps, Job Scheduling, Python (Programming Language), Reliability Engineering, Broadcom, Ansible, Prometheus, Shell Script, Data Logging, Cloud Platform System, Snowflake, Grafana, Build Management, Kubernetes, Apache Kafka, Celery, Terraform, Docker, Control M
- **Published:** August 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=99c56c74489218e9

## About the Role

* Bachelor's degree in a technical field or equivalent practical experience
 * 5+ years of experience in SRE, DevOps, or platform engineering roles

## Description

We are looking for a Senior SRE to join our Platform Engineering team where you'll own the reliability, scalability, and operational excellence of our workflow orchestration platforms - primarily Apache Airflow and Broadcom Automic/UC4. This is a hybrid role: roughly half your time will be spent on steady state operations and incident response, and the other half on engineering projects that meaningfully improve the platforms you support., * Serve as a primary escalation point for production support involving Airflow and UC4 - assisting end-user inquiries, incident root cause analysis, and implementing go-forward solutions

 * Own and continuously improve SLOs, SLIs, and error budgets for orchestration platforms
 * Monitor platform health, capacity, and performance; proactively identify and remediate issues before they impact users
 * Partner with data engineering and application teams to troubleshoot DAG failures, job dependencies, and scheduling issues
 * Manage patching, upgrades, and configuration management for Airflow and UC4 environments
 * Collaborate with security to harden platform configurations and manage software vulnerabilities
 * Contribute to on-call rotations and maintain runbooks and escalation procedures, * Design and build tooling and automation to reduce toil and improve developer experience for teams that depend on Airflow and UC4 (among other platforms the team offers)
 * Lead or contribute to platform modernization initiatives- e.g., migrating workloads, improving deployment pipelines, containerizing components, or adopting managed service offerings
 * Develop and maintain infrastructure-as-code (Terraform, Helm, Ansible, etc.) for platform components
 * Build observability solutions (e.g., dashboards, alerting, log aggregation) that give teams better visibility into their workflows
 * Build and enforce standards around platform use that help engineering teams adopt best practices at scale
 * Participate in design reviews and contribute to the overall platform roadmap, * Deep hands-on experience with Apache Airflow - ideally including distributed executor configurations (Celery or Kubernetes), DAG authoring best practices, and multi-environment deployments
 * Experience operating enterprise job scheduling platforms (e.g., Automic/UC4, Control-M, etc.)
 * Strong Linux and Windows systems knowledge and comfort working in cloud environments (AWS preferred)
 * Proficiency in Python for automation and tooling; familiarity with shell scripting
 * Experience with container orchestration (Kubernetes, Docker) and CI/CD pipelines
 * Solid understanding of observability principles - metrics, logging, tracing - and tools like ELK, Grafana, and Prometheus
 * Demonstrated ability to drive incidents to resolution and communicate clearly under pressure
 * A bias toward automation and a low tolerance for repetitive manual work, * Experience with managed Airflow offerings (e.g., AWS MWAA, Cloud Composer, Astronomer)
 * Familiarity with data platform ecosystems (e.g., dbt, Kafka, Snowflake)
 * Experience migrating workloads from legacy schedulers to modern orchestration tools
 * Prior experience defining and managing error budgets and SLO frameworks


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