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# Data Platform Engineer

- **Company:** Insight Global
- **Location:** Austin, TX, United States
- **Experience:** Experienced
- **Salary:** $85,280.0 - $106,080.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Big Data, Cloud Computing, Continuous Integration, Data Infrastructure, Distributed Data Store, Hadoop Distributed File System, Python (Programming Language), Cloud Services, Jupyter Notebook, Datadog, Cloud Monitoring, Grafana, Apache Spark, HybridCloud, Gitlab, Kubernetes, Infrastructure Automation Frameworks, Apache Flink, Data Management, Terraform, Splunk, Api Management, Docker, Programming Languages
- **Published:** July 8, 2026
- **Apply:** https://jobs.insightglobal.com/jobs/find_a_job/texas/austin/data-platform-engineer/job-548809/

## About the Role

* 5+ years of professional experience with large-scale data platform, data product development, and platform operations
 * 2+ years of experience developing and operating infrastructure, data platforms, and cloud services in hybrid environments (On-prem, AWS, or GCP)
 * Hands-on experience with operations automation and observability on HDFS and Objectstores
 * Experience with Jupyter Notebook environment automation and operations
 * Proficient in one or more programming languages (Python, Java, or Scala) for automation, API integrations, and Spark job development
 * Proficient with analytics stack including Apache Spark/Flink, Iceberg, Airflow, and Kubernetes

Nice to Have Skills & Experience

 * Knowledge of CI/CD technologies including GitLab, Terraform, Docker/Kubernetes, and cloud monitoring tools (Splunk, Grafana, Datadog)
 * Experience implementing security controls, governance processes, compliance validation, infrastructure cost analysis, and optimization

## Description

We are seeking a highly skilled Data Platform Engineer to design, build, automate, and support enterprise-scale data platforms across hybrid cloud environments. This role will focus on platform engineering, infrastructure automation, observability, and the operational excellence of modern analytics ecosystems supporting large-scale data products., The ideal candidate will have deep experience managing distributed data platforms, cloud infrastructure, and big data technologies, while leveraging automation and engineering best practices to enhance reliability, scalability, and performance.


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