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 service 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.

Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field. Master’s degree or equivalent experience preferred. Experience:  Minimum 5 years of experience as a Software Engineer Technical Skills: Proficient in Java, angular or any javascript technology with experience in designing and deploying cloud-based data pipelines and microservices using GCP tools like BigQuery, Dataflow, and Dataproc. Ability to leverage best in-class data platform technologies to deliver platform features, and design & orchestrate platform services to deliver data platform capabilities. Service-Oriented Architecture and Microservices: Strong understanding of SOA, microservices, and their application within a cloud data platform context. Develop robust, scalable services using Java Spring Boot, Python, Angular, and GCP technologies. Full-Stack Development: Knowledge of front-end and back-end technologies, enabling collaboration on data access and visualization layers (e.g., React, Node.js). Design and develop RESTful APIs for seamless integration across platform services. Implement robust unit and functional tests to maintain high standards of test coverage and quality. Database Management: Experience with relational (e.g., PostgreSQL, MySQL) and NoSQL databases, as well as columnar databases like BigQuery. Data Governance and Security: Understanding of data governance frameworks and implementing RBAC, encryption, and data masking in cloud environments. CI/CD and Automation: Familiarity with CI/CD pipelines, Infrastructure as Code (IaC) tools like Terraform, and automation frameworks. Manage code changes with GitHub and troubleshoot and resolve application defects efficiently. Ensure adherence to SDLC best practices, independently managing feature design, coding, testing, and production releases. Problem-Solving: Strong analytical skills with the ability to troubleshoot complex data platform and microservices issues. Certifications (Preferred): GCP Data Engineer, GCP Professional Cloud 

 

Design and Build Data Pipelines: Architect, develop, and maintain scalable data pipelines and microservices that support real-time and batch processing on GCP. Service-Oriented Architecture (SOA) and Microservices: Design and implement SOA and microservices-based architectures to ensure modular, flexible, and maintainable data solutions. Full-Stack Integration: 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. Data Ingestion and Integration: Lead the ingestion and integration of data from various sources into the data platform, ensuring data is standardized and optimized for analytics. GCP Data Solutions: Utilize GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms that meet business needs. Data Governance and Security: 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. Collaboration and Best Practices: Work closely with data architects, software engineers, and cross-functional teams to define best practices, design patterns, and frameworks for cloud data engineering. Automation and Reliability: Automate data platform processes to enhance reliability, reduce manual intervention, and improve operational efficiency.