全职 Solutions Engineer _Python,Snowflack,Java 工作, 薪水 up to SGD 8,000, SPI GLOBAL CONTENT HOLDING PTE. LTD. Central Region (Singapore) 公司招聘中 - Ricebowl

We are seeking a hands-on Senior Solution Engineer to design, build, and operate robust cloud-native data and AI solutions. The ideal candidate combines strong software engineering fundamentals with deep practical experience in AWS and Snowflake

Responsibilities

• Design & Build Solutions: Deliver production-grade software and data solutions end-to-end, from architecture to deployment and continuous improvement.

• Data Engineering: Implement scalable, secure, and well-governed data pipelines across structured, semi-structured, and unstructured sources.

• Snowflake Optimization: Model, curate, and optimize datasets, schemas, and structures for performance and usability.

• Software Engineering Practices: Apply clean code, modular design, automated testing, CI/CD, observability, and secure development.

• Stakeholder Collaboration: Translate requirements into robust solutions and enable advanced analytics and AI use cases.

• AI-Assisted Development: Use tools like GitHub Copilot, Claude, or Cursor to accelerate coding, testing, and documentation.

• Cloud-Native Integrations: Build integrations and automation using AWS compute, storage, networking, and managed AI services.

• Operations Ownership: Manage deployment, release, monitoring, troubleshooting, and incident resolution.

Required

7+ years of professional experience in software, solution, or data engineering roles.

Proven track record of delivering production-grade systems in enterprise environments.

Deep hands-on AWS experience (compute, storage, networking, IAM, security, orchestration, monitoring, serverless/event-driven architectures).

Deep hands-on Snowflake experience (data modeling, SQL tuning, pipeline integration, governance, cost/performance optimization).

Strong proficiency in Python and/or Java with solid software design principles.

Familiarity with AWS AI services (e.g., Amazon Bedrock) and agent-based AI solution patterns.

Experience with AI-assisted engineering tools (Copilot, Claude, Cursor, etc.).

Knowledge of CI/CD, infrastructure-as-code, DevOps practices, and production support.

Ability to translate ambiguous business problems into clear technical scopes and measurable success criteria.