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# Data Platform Engineer

- **Company:** Saige Partners
- **Location:** United States
- **Experience:** Expert
- **Salary:** $113,000.0 - $158,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Data Analysis, Application Frameworks, Microsoft Azure, Big Data, BigQuery, Cloud Storage, Continuous Integration, Information Engineering, DevOps, Data Flow Control, Github, Python (Programming Language), Systems Development Life Cycle, Cloudera, SQL Databases, Enterprise Data Management, Scripting, Google Cloud, Data Ingestion, System Availability, Build Server, Data Layers, AI Platforms, Kubernetes, Information Technology, Deployment Automation, Software Version Control, Jenkins
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/d33c9167-5ba1-4d72-a12c-d3ea55b92a74

## About the Role

* Bachelor s degree in Computer Science, Engineering, MIS, or related field.
 * 7+ years of experience with enterprise data, analytics, cloud, DevOps, ingestion, orchestration, or related platforms.
 * Strong experience with CI/CD, automation, SDLC standards, and platform roadmaps.
 * Python, SQL, or scripting experience.
 * Experience building reusable tools, frameworks, and developer enablement solutions.
 * Strong communication, problem-solving, and cross-functional collaboration skills.
 * Ability to influence technical direction across teams.

Preferred

 * Google Cloud Platform experience with BigQuery, Cloud Storage, Composer/Airflow, Pub/Sub, Dataflow, Dataproc, Kubernetes/GKE, Cloud Build, or similar services.
 * Experience with Azure DevOps, GitHub Actions, Jenkins, or similar CI/CD tools.
 * Experience with APIs, large-scale data ingestion, semantic layers, analytics, or AI platforms.
 * Experience supporting enterprise Data & Analytics organizations.

## Description

* Drive the roadmap and continuous improvement of data ingestion, orchestration, and platform tools.
 * Build CI/CD, deployment automation, reusable frameworks, and self-service tools.
 * Support and enhance Python-based data ingestion frameworks.
 * Establish SDLC, source control, deployment, and engineering standards.
 * Improve platform reliability, monitoring, observability, and developer productivity.
 * Partner with Data Engineering, Architecture, Infrastructure, Security, Governance, and Analytics teams.
 * Evaluate new technologies and help shape enterprise data platform strategy.
 * Create technical documentation, onboarding materials, and operational standards.


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