What you'll be doing

Design, develop, test, and maintain reliable software solutions for analytics products across the full software development lifecycle.
Apply system design, low-level design, API design, and data modelling to build scalable, maintainable services and product capabilities usable by people and AI agents.
Collaborate with engineers, product managers, analysts, and stakeholders to translate business and analytical requirements into effective technical solutions focused on user decisions.
Diagnose complex technical issues, improve application performance and reliability, and enhance operational excellence through monitoring, testing, and continuous improvement.
Develop technical aptitude in backend services, data-intensive workflows, and AI-powered analytics products applying engineering practices.
Safely integrate and operate AI/ML-enabled solutions using certified metrics and ensuring output accuracy.
Build AI-powered analytics features such as conversational analytics, natural-language-to-SQL, automated insight summaries, and self-service decision tools.
Test new ideas rapidly, ship early, and turn successful experiments into reusable, self-service capabilities for broader use.

Requirements

Bachelor’s degree in Computer Science, Data Science, or a related technical field, or equivalent practical experience.
2+ years of professional software development experience delivering production-quality applications or services.
Experience owning and delivering features or services within analytics, data, or product engineering domains including implementation, testing, troubleshooting, and maintenance.
Proficiency in software engineering fundamentals such as programming, version control, automated testing, API development, data modelling, and system design.
Bias to action and ownership, moving unclear problems from concept to reliable solution quickly and collaborating effectively with technical and non-technical teams.
Foundational understanding of analytics and data, including metrics, data modelling, and data-driven decision processes.
Hands-on experience using AI-assisted development tools with a habit of reviewing generated code for correctness, security, and maintainability.

Experience building and operating analytics products or data-intensive services at scale with attention to performance, availability, and maintainability.
Ability to contribute to architectural and technical design decisions within a service or multi-service product area.
Experience with observability, automation, testing, and continuous delivery practices to improve operational excellence.
Use of data and product signals to evaluate technical outcomes, identify improvements, and guide engineering decisions.
Hands-on exposure to AI/ML-enabled products or workflows such as agents, large language models, retrieval-augmented generation, or MCP tools, including integration and safe production operation.
Attention to analytics user experience, e.g., showing uncertainty, citing metrics, or explaining changes.
Experience with front-end or full-stack development building interactive, user-facing products.