Full Stack Software Engineer

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:

• Technical Skills: Proficient in Java or Python , 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 (Apache Beam, Kafka, …) 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, ).
• 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.