CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.
Why us?
CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally.
In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.
We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one.
With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.
We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.
Set up the development environment, contribute first PRs under guidance, and build enough architectural understanding to describe the end-to-end pipeline — event ingestion, Data Lake zones, and REST API serving — clearly to a peer.
Deliver well-scoped tasks independently — bug fixes, test coverage gaps, and minor API endpoint additions — with the first Data Lake Bronze zone write task completed and consistent participation in squad ceremonies.
Maintain consistent delivery quality on medium-scope tasks, write tests without being prompted, and explain event consumer logic and REST API code to a new joiner without assistance. Complete AZ-900 certification.
Contribute a documented technical input to at least one system design discussion and demonstrate junior IC3-level autonomy across delivery, testing, and code quality — ready for formal level progression review.
Implement bug fixes, small features, and test improvements in Python/FastAPI/Django under structured senior guidance.
Write unit tests (pytest) for all developed code; learn to read coverage reports and identify branch coverage gaps.
Study and apply squad coding standards: event schema conventions, Pydantic model patterns, REST API response formats, and Data Lake write templates.
Participate in pair programming sessions as both driver and navigator, with defined learning goals each session.
Read and document existing event consumer code, Data Lake transformation pipelines, and REST API implementations as part of onboarding.
Attend all architecture reviews and design discussions as an active observer — taking notes, asking clarifying questions, and summarising key decisions in the squad wiki.
Shadow senior engineers during system design whiteboarding — learning to read sequence diagrams, event flow maps, and Data Lake zone diagrams.
Study the Lane 01 architecture map — understanding each microservice, event flow, Data Lake zone, and REST API surface in the full pipeline.
Complete one self-directed study of a core architectural pattern per quarter (e.g. Bronze/Silver/Gold Data Lake model, event-driven saga, API versioning strategy) and present a short summary to the squad.
Learn RabbitMQ consumer patterns by working within existing implementations — understanding exchange types, routing keys, acknowledgement modes, and dead-letter queue behaviour.
Implement Bronze zone write tasks: land raw documents to ADLS Gen2 with correct metadata and partitioning under senior guidance.
Add Silver zone data quality validation rules — field validation checks and schema assertions — to existing transformation pipelines.
Learn to read Event Hubs consumer lag metrics and offset commits during on-call shadow rotations.
Implement minor FastAPI endpoint additions under senior review — applying Pydantic response models, HTTP status code conventions, and OpenAPI documentation standards.
Write integration tests for assigned endpoints covering happy path, validation error, and auth failure scenarios.
Contribute to OpenAPI documentation updates — parameter descriptions, example payloads, and error code references.
Learn OAuth2/Azure AD token validation by working within existing FastAPI authentication middleware.
Participate in code reviews — observer initially, then structured reviewer within 60 days.
Complete mandatory certifications within 6 months: Microsoft Azure Fundamentals (AZ-900) and one Python testing or data engineering foundations course.
Submit a monthly technical learning note to the squad wiki — a concept studied with a practical codebase example.
Proactively surface blockers in standups; never sit on a blocker more than one working day without flagging.
0–3 years of professional Python development (internships, contract work, and strong academic projects count).
Solid Python fundamentals: data structures, OOP, error handling, list comprehensions, and basic async awareness.
Exposure to at least one web framework — Django, FastAPI, Flask, or similar.
REST API basics: HTTP methods, status codes, JSON request/response, and understanding of what an API endpoint does.
Git fundamentals: commit, branch, pull request, and basic merge conflict resolution.
Strong self-directed learning instinct — able to demonstrate independently acquired technical skills with examples.
Clear written English: PR descriptions, code comments, and documentation.
Any hands-on exposure to event-driven systems — message queues, pub/sub, async tasks — in academic or project context.
Basic Data Lake or data pipeline awareness: ETL, CSV/JSON file processing, any cloud storage interaction.
Docker at basic level: build and run a container.
Azure exposure at any level: AZ-900 coursework, hands-on projects, or certification.
Open-source contributions or a personal Python project portfolio.
Interested?
If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.
CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
