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We are looking for a hands-on senior solution engineer who can 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.

You will have the following responsibilities:

 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.

 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.

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

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

 Partner with business and technical stakeholders to translate requirements into robust data

solutions, prioritise delivery, and identify opportunities to enable advanced analytics and AI use cases.

 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.

 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.

 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.

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.

 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.

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

 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

 Strong proficiency in Java, with solid understanding of software design principles, APIs,

automated testing, packaging, dependency management, and production maintainability.

 Experience with AWS AI services, including Amazon Bedrock, and familiarity with agent-based AI solution patterns, retrieval-augmented generation, model evaluation, guardrails, and responsible AI practices is preferred. Demonstrated habit of using AI-assisted engineering tools such as GitHub Copilot, Claude, Cursor, or similar tools as part of everyday development to improve productivity, code quality, testing, documentation, and delivery speed.

 Familiarity with harness engineering or similar AI-assisted development concepts, including structuring prompts, evaluation loops, reusable development workflows, automated checks, and feedback mechanisms to improve reliability, repeatability, and engineering quality.

 Strong hands-on engineering mindset, with a focus on code quality, sound design decisions, maintainability, and effective collaboration in team-based environments.

 Strong familiarity with the software development lifecycle, Git-based workflows, CI/CD,

infrastructure-as-code concepts, automated testing, DevOps practices, and production support.

 Ability to translate ambiguous business problems into clear technical scopes, iterative delivery plans, and measurable success criteria.

 Comfortable working with sensitive and confidential data, and partnering with governance, risk, and security stakeholders to embed controls from the start.

 Strong collaboration and communication skills, with the ability to work closely with business stakeholders and cross-functional technology teams.

 Preferred: background in the financial industry, with an understanding of financial markets, data sensitivity, regulatory expectations, and enterprise risk controls.