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# Platform Engineer

- **Company:** OpenKyber LLC
- **Location:** Atlanta, GA, United States
- **Contract:** Temporary to permanent
- **Skills:** Query Performance, Amazon Web Services, Apache HTTP Server, Application Frameworks, Microsoft Azure, Cloud Computing, Cloud Database, Software Quality, Computer Programming, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Distributed Data Store, Distributed Systems, Python (Programming Language), Meta-Data Management, Scala (Programming Language), Software Construction, Data Streaming, Data Logging, Google Cloud, Autoscaling, System Availability, Apache Spark, Kubernetes Helm Charts, Infrastructure as Code (IaC), Containerization, Kubernetes, Deployment Automation, Data Management, Cloud Optimization, Data Pipelines, Docker
- **Published:** October 14, 2026
- **Apply:** https://www.careerjet.com/jobad/us1e965680f7afdf79369d7ced6bbeb228

## About the Role

* Strong hands-on experience with Apache Iceberg and lakehouse architecture.
 * Strong experience with Trino and distributed query processing.
 * Strong experience with Apache Spark .
 * Strong programming experience in Scala and Python .
 * Hands-on experience with Kubernetes and containerized workloads.
 * Experience with at least one major cloud platform: AWS, Azure, or Google Cloud Platform .
 * Experience with cloud object storage and distributed data platforms.
 * Experience with Helm, CI/CD, Infrastructure as Code, monitoring, and observability .
 * Strong understanding of distributed systems, data engineering, and platform engineering.
 * Experience building scalable, reliable, and high-performance data pipelines.

Preferred Qualifications

 * Experience operating production-scale lakehouse platforms.
 * Experience with streaming data architectures.
 * Experience with data governance, security, and access-control frameworks.
 * Experience optimizing distributed workloads for performance and cloud cost.
 * Strong software engineering fundamentals, including testing, code quality, and automation.

## Description

We are seeking an experienced Data Platform Engineer to architect, build, and operate a scalable cloud-based data lakehouse platform . The ideal candidate will have strong hands-on experience with Apache Iceberg, Trino, Apache Spark, Scala, Python, Kubernetes, and cloud infrastructure . This role will focus on building reliable and high-performance data platforms that support analytics, data science, and downstream applications at scale. Key Responsibilities

 * Design, implement, and maintain a cloud-based data lakehouse using Apache Iceberg .
 * Manage Iceberg table lifecycle, including partitioning, schema evolution, snapshots, metadata management, compaction, time travel, and ACID transactions .
 * Optimize table layouts, file sizes, query performance, storage efficiency, and cloud costs.
 * Deploy, configure, tune, and operate Trino clusters for interactive and federated analytics.
 * Manage Trino connectors, catalogs, resource groups, concurrency, and workload performance.
 * Develop and optimize large-scale batch and streaming workloads using Apache Spark .
 * Troubleshoot distributed query and processing issues, including query plans, spills, shuffle bottlenecks, and job performance .
 * Develop scalable data pipelines and reusable frameworks using Scala and Python .
 * Build ingestion, transformation, and data-quality frameworks for batch and streaming environments.
 * Integrate data pipelines with Apache Iceberg, Trino, and Spark .
 * Design and operate data platform components on Kubernetes , including EKS, AKS, GKE, or self-managed Kubernetes.
 * Build and manage Docker/containerized workloads, Helm charts, and autoscaling .
 * Provision and manage cloud infrastructure across AWS, Azure, or Google Cloud Platform , including object storage and compute resources.
 * Implement Infrastructure as Code (IaC), CI/CD, monitoring, logging, and observability .
 * Ensure platform reliability, scalability, security, and operational efficiency.
 * Automate deployment, testing, and operational processes to reduce manual effort.
 * Collaborate with data engineers, data scientists, analysts, and application teams to improve data accessibility and platform capabilities.
 * Mentor engineers and promote data platform and software engineering best practices.