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(352) FASTTEK | (352) 327-8835
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info@fasttek.com
(352) FASTTEK | (352) 327-8835
Chennai, Tamil Nadu
Software Engineer #1065699
Job Description:
  • 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.
Skills Required:
  • Angular
  • Spring Boot
Skills Preferred:
  • Big Query
  • Data Flow
  • Dataproc
Experience Required:
  • Engineer Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang.
  • 6+ years in IT;
  • 4+ years in development
  • o 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.
  • o 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.
  • o 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.
  • o Full-Stack Development: Knowledge of front-end and back-end technologies, enabling collaboration on data access and visualization layers (e.g., React, Node.js).
  • o Design and develop RESTful APIs for seamless integration across platform services.
  • o Implement robust unit and functional tests to maintain high standards of test coverage and quality.
  • o Database Management: Experience with relational (e.g., PostgreSQL, MySQL) and NoSQL databases, as well as columnar databases like BigQuery.
  • o Data Governance and Security: Understanding of data governance frameworks and implementing RBAC, encryption, and data masking in cloud environments.
  • o CI/CD and Automation: Familiarity with CI/CD pipelines, Infrastructure as Code (IaC) tools like Terraform, and automation frameworks.
  • o Manage code changes with GitHub and troubleshoot and resolve application defects efficiently.
  • o Ensure adherence to SDLC best practices, independently managing feature design, coding, testing, and production releases.
  • o Problem-Solving: Strong analytical skills with the ability to troubleshoot complex data platform and microservices issues.
Experience Preferred:
  • GCP Data Engineer
  • GCP Professional Cloud
Education Required:
  • Bachelor's Degree
Additional Info:
  • 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.
  • AI/ML: Leverage AI to accelerate delivery and solve business problems at the platfrom level.