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
