Software Engineer

• Service-Oriented Architecture and Microservices: Strong understanding of SOA, microservices, and their application within a cloud data platform context.
• Full-Stack Development: Knowledge of front-end and back-end technologies, enabling collaboration on data access and visualization layers (e. g. Angular, React, Node. js).
• 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.
• Problem-Solving: Strong analytical skills with the ability to troubleshoot complex data platform and microservices issues.

Qualification-Btech, Mtech

Key Job responsibilities:

• 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.