Senior Solution Engineer - AI and Snowflake

Key Responsibilities

1. Design, build, and operate production-grade software and data solutions end-to-end, from problem definition and architecture through implementation, deployment, monitoring, and continuous improvement.

2. Design and implement reliable, scalable, secure, and well-governed data pipelines and data products using AWS and Snowflake across structured, semi-structured, and unstructured data sources.

3. Model, curate, and optimize Snowflake datasets, schemas, and data structures in line with enterprise platform standards, ensuring performance, quality, consistency, and usability for downstream consumers.

4. Apply strong software engineering practices, including clean code, modular design, automated testing, CI/CD, observability, secure development, and maintainable architecture.

5. Partner with business and technical stakeholders to translate requirements into robust data solutions, prioritize delivery, and identify opportunities to enable advanced analytics and AI use cases.

6. Use AI-assisted engineering as a standard part of daily development work to accelerate coding, refactoring, documentation, testing, debugging, and solution exploration while maintaining strong engineering judgement and quality standards.

7. Build cloud-native integrations and automation on AWS, making effective use of services such as compute, storage, networking, security, orchestration, event-driven architectures, and managed AI services where appropriate.

8. Own deployment, release, and production operations, including troubleshooting, root-cause analysis, performance tuning, incident resolution, peer code reviews, pair programming, and reuse of proven engineering patterns.

Skills and Experience Required

1. Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence / Machine Learning, or a related technical discipline.

2. 7+ years of professional experience in a hands-on software engineering, solution engineering, or data engineering role, with a proven track record of delivering production-grade systems in enterprise environments.

3. Demonstrated ability to build and operate data products, cloud services, or AI-enabled solutions with measurable business outcomes and clear operational ownership.

4. Deep hands-on AWS experience is required, including practical knowledge of core services for compute, storage, networking, identity and access management, security, orchestration, monitoring, and serverless or event-driven architectures. AWS certification is preferred, ideally AWS Certified Solutions Architect – Associate, AWS Certified Data Engineer – Associate, or AWS Certified Machine Learning Engineer – Associate.

5. Deep hands-on Snowflake experience is required, including data modelling, SQL performance tuning, pipeline integration, access control, cost/performance optimization, data sharing, and platform governance. SnowPro® Core Certification or advanced Snowflake certifications are a plus.