Senior Java (with microservices, AWS) - BGC - Up to 200K - Hybrid

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.