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 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 good engineering judgement and quality standards.
Build cloudnative 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, ideally AWS Certified Solutions Architect
Solid 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 .
Background in the financial industry, with an understanding of financial markets, data sensitivity, regulatory expectations, and enterprise risk controls.
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.

EA License : 02C3423

EA Personnel : R22108699

Senior Solution Engineer - Java