Platform Software Engineer - Dearborn - ref. i5021115

apartmentStefanini, Inc placeDearborn calendar_month 

Stefanini Group is hiring!

Stefanini is looking for a Platform Software Engineer, Dearborn, MI (Hybrid)

For quick apply, please reach out Anmol Tyagi at 248-263-8628/anmol.tyagi@stefanini.com

A Platform Software Engineer is a versatile developer with expertise in Java or Python and a strong foundation in cloud platforms to build and manage applications at scale. Generally, platform engineers fall into two categories: backend engineers, who design and implement microservices with robust APIs, and full-stack engineers, who deliver native UI/UX solutions, and ability to develop frameworks and services to enable an enterprise data platform.

With a solid understanding of the SDLC and hands-on experience in Git and CI/CD, platform engineers can independently design, code, test, and release features to production efficiently.

Responsibilities-

Architect, develop, and maintain scalable data pipelines and microservices that support real-time and batch processing on GCP.-

Design and implement SOA and microservices-based architectures to ensure modular, flexible, and maintainable data solutions.-

Leverage your full-stack expertise to contribute to the seamless integration of front-end and back-end components, ensuring robust data access and UI-driven data exploration.-

Lead the ingestion and integration of data from various sources into the data platform, ensuring data is standardized and optimized for analytics.-

Utilize GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms that meet business needs.-

Implement and manage data governance, access controls, and security best practices while leveraging GCP's native row- and column-level security features. Performance Optimization: Continuously monitor and improve the performance, scalability, and efficiency of data pipelines and storage solutions.-

Work closely with data architects, software engineers, and cross-functional teams to define best practices, design patterns, and frameworks for cloud data engineering.-

Automate data platform processes to enhance reliability, reduce manual intervention, and improve operational efficiency.

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