Stefanini is looking for a Platform Software Engineer, Dearborn, MI
For quick apply, please reach out to Adil Khan at 248-728- 6424/[email protected]
A Platform Software Engineer is a versatile developer with expertise in Java or Python and a strong foundation in cloud platforms, responsible for building and managing applications and data platforms at scale. Platform engineers may focus on backend development, designing and implementing microservices and robust APIs, or full-stack development, delivering UI/UX solutions and frameworks that enable an enterprise data platform. The engineer should have a strong understanding of the SDLC and hands-on experience with Git and CI/CD, with the ability to independently design, develop, test, troubleshoot, and release features to production.
Responsibilities
Design and Build Data Pipelines: Architect, develop, and maintain scalable data pipelines and microservices supporting real-time and batch processing on GCP.
Service-Oriented Architecture and Microservices: Design and implement SOA and microservices architectures to deliver modular, flexible, scalable, and maintainable data solutions.
Full-Stack Integration: Contribute to the integration of front-end and back-end components, supporting robust data access and UI-driven data exploration.
Data Ingestion and Integration: Lead the ingestion and integration of data from multiple sources into the enterprise data platform, ensuring data is standardized and optimized for analytics.
GCP Data Solutions: Utilize GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Functions to build and manage scalable data platform solutions.
Data Governance and Security: Implement data governance, access controls, and security best practices, including GCP row-level and column-level security.
Performance Optimization: Monitor and improve the performance, scalability, reliability, and efficiency of data pipelines and storage solutions.
Collaboration and Best Practices: Partner with data architects, software engineers, and cross-functional teams to establish best practices, design patterns, and frameworks for cloud data engineering.
Automation and Reliability: Automate data platform processes to improve reliability, reduce manual intervention, and increase operational efficiency.
AI/ML: Leverage AI/ML capabilities and tools to accelerate software delivery and solve business problems at the platform level.