Staff Back-End Engineer

Cube is the financial intelligence platform built for the AI-era. The world has evolved, but FP&A is still trapped in manual reconciliation cycles. Cube unifies fragmented data into a centralized command center, automating data management to empower the Office of the CFO to focus on strategic analysis and growth.

Cube doesn’t compete with AI; it makes AI trustworthy. In a world where AI is often only 80% accurate, Cube provides 100% truth and governance for finance teams to drive growth.

About the Role

We are seeking a Staff Back-End Engineer to serve as a technical North Star for our engineering organization. This role involves shaping architectural patterns, infrastructure standards, and engineering habits to enable the entire team to scale.

Reporting to an Engineering Manager, you will act as a highly strategic individual contributor working across multiple problem spaces. You’ll partner closely with Engineering, Product, and Customer Success to transform ambiguous business needs into robust distributed systems that power real financial workflows.

This role exists at a pivotal moment in Cube’s growth. As our platform expands in complexity and scale, we’re looking for someone who can elevate system design, improve long-term reliability, and help define what great engineering looks like across the organization.

You will take ownership of complex initiatives end-to-end, influencing infrastructure, deployment strategies, observability practices, and long-term system health.

Tech Stack

• Python (Django + DRF)

• MySQL, ClickHouse, Redis, RabbitMQ, Celery

• AWS (deployed via CDK), CI/CD automation

• JavaScript/TypeScript (React/Redux) on the front end

• AI-driven workflows, LLM agents, enterprise integrations, and advanced observability

Who You Are

• Experienced Technical Leader: Typically requires 10+ years of professional software engineering experience, with flexibility for exceptional candidates who demonstrate strong Staff-level impact, ownership, and technical leadership.

• 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.

• Python/Django Expert: Deep knowledge of Django internals including middleware, database routing, management commands, signals, and performance optimization in high-scale, multi-tenant environments.

• 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.

• 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.

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

• Effective Communicator: Able to clearly explain architectural trade-offs to executives, product partners, and engineers at all levels.

• Nice to Have: Familiarity with FP&A domain concepts such as trial balances, charts of accounts, fiscal calendars, scenario modeling, and multi-currency workflows.

How You’ll Make an Impact

Technical Leadership

• Define the Standard: Set architectural direction and establish engineering patterns that become the golden path across teams.

• Own the Core: Design and evolve complex backend systems including multi-tenant database routing, distributed task orchestration, async pipelines, and enterprise integrations.

• Modernize Workflows: Lead adoption of AI-assisted development practices and LLM agent systems within production environments.

Execution & Excellence

• Ship Strategic Initiatives: Take ambiguous product goals and deliver them end-to-end, from infrastructure (CDK) and deployment to monitoring and documentation.

• Observability First: Foster a culture of observability by default, owning strategies around structured logging, APM tooling, metrics, and production debugging workflows.

• High-Stakes Resolution: Act as a steady technical leader during production incidents, guiding root-cause analysis and long-term remediation strategies.

Mentorship & Culture

• Elevate the Team: Remove technical blockers, mentor engineers, and promote psychological safety alongside a culture of continuous improvement.

• Scale the Org: Continuously improve CI/CD pipelines, testing infrastructure, release processes, and deployment strategies so teams can ship safely at speed.