Senior Solution Engineer

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 Associate, AWS Certified Data Engineer – Associate, or AWS Certified Machine

Learning Engineer – Associate.

• Deep hands-on Snowflake experience is required, including data modelling, SQL performance tuning,

pipeline integration, access control, cost/performance optimisation, data sharing, and platform

governance. SnowPro® Core Certification or advanced Snowflake certifications are a plus.

• Strong proficiency in Python and/or 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.Role & responsibilities

Preferred candidate profile