TEAM International is hiring for a Snowflake Data Engineer!
Role Highlights
- You must be willing to work as a full-time independent contractor (no contract end date; you will manage your own contributions)
- You must be willing to report to a hybrid setup for at least 2-3x onsite per week in BGC
- Working schedule is from Monday to Friday, with a flexible working schedule from morning to afternoon shift, Manila time
About Us:
We're a global IT consulting company and a software development service provider that helps organizations operate at their best. With 30+ years of experience, 6+ global locations, and 1,000+ employees, TEAM combines technology expertise, valuable insights, business intelligence, and a client-centered approach to address challenges in business operations, digital transformation, risk management, compliance, business continuity, and more
To know more about what we do, you may visit our website at: https://www.teaminternational.com/en
About The Role:
We are looking for a motivated Data Engineer with a strong interest in cloud, data, software engineering, and AI-enabled delivery. The ideal candidate has solid technical fundamentals, a learning mindset, and the curiosity to grow hands-on skills in AWS, Snowflake, and modern engineering practices.
You will have the following responsibilities:
- Design, develop, test, deploy, and enhance software and data solutions, working closely with senior engineers and cross-functional teams.
- Build, maintain, and optimize data pipelines using Snowflake and AWS services, ensuring reliability, scalability, and performance.
- Develop and manage Snowflake data solutions, including Dynamic Tables, data models, schemas, transformations, and performance optimization techniques such as micro-partitioning.
- Support data ingestion, transformation, orchestration, and monitoring processes within Snowflake and AWS environments.
- Apply engineering best practices, including clean code, modular design, automated testing, version control, documentation, and maintainable implementations.
- Collaborate with product owners, business stakeholders, and technology teams to understand requirements and deliver high-quality solutions.
- Use AI-assisted engineering tools to support coding, refactoring, documentation, testing, debugging, and solution design.
- Develop and support cloud-native integrations, automation, and data workflows leveraging AWS services such as compute, storage, security, orchestration, and event-driven architectures.
- Participate in deployments, releases, troubleshooting, production support, peer code reviews, and continuous improvement activities.
- Ensure compliance with data security, governance, and access control standards when handling enterprise data assets.
You will have the following qualifications:
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence / Machine Learning, or a related technical discipline.
- Professional experience in software engineering, data engineering, cloud engineering, or a related technical role.
- Strong understanding of software engineering fundamentals, including programming, APIs, testing, debugging, and maintainable code.
- Hands-on experience with Python and/or Java.
- Strong SQL skills and experience with data modeling and data pipelines.
- Advanced Snowflake experience is required, including Dynamic Tables, micro-partitioning, performance optimization, and building end-to-end data pipelines.
- Hands-on experience with AWS cloud services is required.
- Experience with Git, CI/CD, DevOps practices, and automated testing is an advantage.
- Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, Cursor, or similar.
- Exposure to AWS AI services, Amazon Bedrock, RAG, agent-based solutions, or responsible AI practices is a plus.
- Understanding of data security, governance, and access controls.
- Strong problem-solving, collaboration, and communication skills.
Key Must-Haves:
- Advanced Snowflake expertise.
- Experience with Snowflake Dynamic Tables and building data pipelines in Snowflake.