Description :

Job Title : Full Stack Platform Engineer (Data Ecosystem GenAI Development)

Role Objective :

We are looking for a highly skilled Full Stack Platform Engineer to lead the development of our internal Data Platform Portal and integration layers.

The ideal candidate will be responsible for abstracting the complexity of the enterprise data ecosystem (GCP, BigQuery, dbt, Airflow/Composer) through a clean, scalable UI and robust backend APIs, enabling self-service provisioning and seamless platform workflows for technical users.

Core Responsibilities :

Frontend & UI/UX Development :

- Architect and build modular, reusable component libraries for internal developer portals and admin consoles.

- Design intuitive and high-performance interfaces tailored for data engineers and technical users.

- Develop self-service workflows for infrastructure provisioning, template management, and dynamic documentation platforms.

- Implement telemetry and analytics to track user adoption, workflow efficiency, and feature utilization.

- Ensure responsive, accessible, and scalable frontend architecture using modern UI frameworks.

Backend & Integration Engineering :

- Design and develop scalable backend services, REST APIs, and gRPC services to support platform operations.

- Build secure integration layers connecting Cloud Composer (Airflow), Dataplex, BigQuery, dbt, and other cloud-native services.

- Implement authentication and authorization mechanisms including OAuth, OIDC, RBAC, and SSO integrations.

- Ensure high availability, scalability, reliability, and observability of platform services.

- Develop cloud-native microservices and integration workflows for enterprise-scale systems.

Mandatory Technical Skills :

Experience :

- 5+ years of experience in Full Stack Engineering.

- Strong experience building internal platforms, developer portals, enterprise admin consoles, or self-service engineering platforms.

Frontend Skills :

- Strong expertise in React.js / Angular / Vue.js.

- Deep understanding of component-driven architecture and reusable UI libraries.

- Strong experience with TypeScript and modern JavaScript.

- Expertise in state management libraries such as Redux, Zustand, or Vuex.

- Experience with responsive UI design and frontend performance optimization.

- Familiarity with Material UI, Tailwind CSS, or enterprise UI frameworks.

Backend Skills :

- Strong backend development experience using Node.js, Python, Go, or Java.

- Expertise in RESTful API and gRPC service development.

- Experience building scalable microservices architecture.

- Strong knowledge of authentication and security best practices.

- Experience with API gateways and service integrations.

Cloud & Data Ecosystem :

- Hands-on experience with GCP services including BigQuery, Dataplex, Cloud Composer/Airflow.

- Understanding of dbt transformation workflows and orchestration pipelines.

- Experience integrating enterprise data platforms and cloud-native services.

- Familiarity with CI/CD pipelines and Infrastructure as Code.

Database & Messaging :

- SQL and NoSQL database experience.

- Experience with PostgreSQL, BigQuery, or Cloud SQL.

- Understanding of event-driven architecture and messaging systems.

DevOps & Platform Engineering :

- Docker and Kubernetes experience.

- CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI.

- Infrastructure automation using Terraform or similar tools.

- Monitoring and observability using Grafana, Prometheus, or Cloud Monitoring.

GenAI & Advanced Skills :

- Exposure to Generative AI, LLM integrations, and AI-powered platform workflows.

- Understanding of RAG architectures and AI integration patterns is preferred.

- Experience integrating AI APIs into enterprise platforms is an added advantage.

System Design Expectations :

- Ability to design decoupled architectures where frontend systems interact seamlessly with cloud-native APIs and backend microservices.

- Strong understanding of scalability, maintainability, security, and enterprise-grade architecture patterns.

- Experience designing reusable engineering platforms and internal developer tooling.

What Success Looks Like :

Developer Autonomy :

- Reduce the provisioning time for Data Engineers from days to minutes through self-service platform workflows.

Component Reusability :

- Establish scalable and reusable UI abstraction libraries that accelerate internal tool development.

Seamless Integration :

- Enable frictionless communication between orchestration, governance, and execution layers across Composer, Dataplex, BigQuery, and dbt ecosystems.

Platform Scalability :

- Deliver reliable, high-performance, and secure platform services capable of supporting enterprise-scale data operations.