Platform Software Engineer

Stefanini Group•Dearborn, MI

1d•Onsite

About The Position

Stefanini is looking for a Platform Software Engineer, Dearborn, MI. 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.

Requirements

Microservices, Web Services, Full Stack, Database Design, GCP, Data Governance, Information Security, Performance Tuning, Cross-functional Collaboration, Automation, AI/ML, Analytical Skills
Java & Cloud Development: Strong proficiency in Java, Angular, or other JavaScript technologies, with experience designing and deploying cloud-based data pipelines and microservices using GCP services such as BigQuery, Dataflow, and Dataproc.
Data Platform Technologies: Experience with modern data platform technologies such as Apache Beam and Kafka, including designing and orchestrating platform services to deliver scalable data capabilities.
Service-Oriented Architecture & Microservices: Strong understanding of SOA and microservices architecture within cloud data platforms. Experience developing scalable services using Java/Spring Boot, Python, Angular, and GCP.
Full-Stack Development: Knowledge of front-end and back-end technologies, with the ability to collaborate on data access and visualization layers using technologies such as React and Node.js.
API Development: Experience designing and developing RESTful APIs for integration across platform services.
Testing & Quality: Experience developing robust unit and functional tests while maintaining strong test coverage and application quality.
Database Management: Experience with relational databases such as PostgreSQL and MySQL, NoSQL databases, and columnar databases such as BigQuery.
Data Governance & Security: Understanding of data governance frameworks and experience implementing RBAC, encryption, and data masking in cloud environments.
CI/CD & Automation: Familiarity with CI/CD pipelines, Terraform/IaC, and automation frameworks.
Source Control & Troubleshooting: Experience using GitHub to manage code changes and troubleshoot and resolve application defects.
SDLC: Ability to independently manage the full software development lifecycle, including feature design, development, testing, and production releases while following SDLC best practices.
Problem-Solving: Strong analytical and troubleshooting skills with the ability to resolve complex data platform and microservices issues.

Nice To Haves

Reliability Engineering experience
GCP Data Engineer, GCP Professional Cloud

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.