Staff Backend Enginee

Join Cube, a fast-growing financial technology company, as a Staff Back-End Engineer. In this pivotal role, you will shape the architectural patterns, infrastructure standards, and engineering habits that allow our entire team to scale. You will take ownership of complex initiatives, influence infrastructure and deployment strategies, and foster a culture of observability. This is an opportunity to help build scalable engineering foundations and accelerate the entire team.

Lead the adoption of AI-assisted development practices and LLM agent systems within production environments.

The expectations above are meant to represent the ideal candidate, but if you don’t meet all of them and think you’d be a great fit for this role, please apply

About Cube

Efficiency Minded: Early adopter of AI-assisted engineering workflows (Cursor, Claude Code, automated refactoring, or similar tooling) to increase velocity without sacrificing reliability or code quality

Responsibilities

• Define the architectural direction and establish engineering patterns that become the standard across teams.
• Design and evolve complex backend systems, including multi-tenant database routing, distributed task orchestration, and enterprise integrations.
• A Technical Strategist: You have clear, experience-backed opinions on distributed systems and long-term architecture. You don’t just solve today’s problem; you design systems that support years of growth

Requirements

• Systems Architect: Strong experience building and evolving distributed systems across cloud infrastructure. Comfortable debugging complex concurrency challenges such as race conditions, distributed deadlocks, async failures, and large-scale database performance issues
• Experienced Technical Leader: Typically brings 10+ years of professional software engineering experience, with flexibility for exceptional candidates who demonstrate strong Staff-level impact, ownership, and technical leadership
• Effective Communicator: Able to clearly explain architectural trade-offs to executives, product partners, and engineers at all levels
• Python/Django Expert: Deep knowledge of Django internals including middleware, database routing, management commands, signals, and performance optimization in high-scale, multi-tenant environments
• Data Fluent: Experience with dataframe-based computation tools such as Pandas, Polars, or NumPy to power data-intensive workflows and financial modeling pipelines
• Engineering Leader: Proven ability to define engineering standards, design reusable abstractions, and improve developer velocity through internal tooling and frameworks
• Nice to Have: Familiarity with FP&A domain concepts such as trial balances, charts of accounts, fiscal calendars, scenario modeling, and multi-currency workflows