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