Senior Staff Backend Engineer Forward Deployment Engineering

What the role needs

• What makes this role different
• 8+ years of backend engineering in Python, Go, Java, or similar, including significant time at Staff+ level owning complex, data-intensive systems.
• Production Python with strong service design and testing practices.
• Elite SQL and data modeling. You’re fluent in window functions and large-scale joins, and you reason rigorously about both correctness and performance.
• Set-based, reproducible computation over a data warehouse such as Snowflake or PostgreSQL.
• Configuration-driven architecture. You’ve designed extensible services or rules engines where new use cases ship as configuration, not new code paths.
• Strong system design for reliability, isolation, and reproducibility.
• A correctness mindset. You treat “every result is traceable, explainable, and reproducible” as a hard requirement.
• Technical leadership. You set patterns, review designs, and mentor engineers.

From the employer

About Meeru AI Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders. We deploy on customer terms — SaaS multi-tenant, SaaS single-tenant, and on-premises — across AWS, Azure, and GCP . Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance. The Role We are looking for a Senior Staff Backend Engineer to own the core services at the heart of the platform — the data-intensive computation layer that turns curated financial and operational data into correct, reproducible, fully traceable results. This is the structured engineering beneath the product: not dashboards, not an LLM wrapper, but the services that do the heavy computation and stand behind every number the platform produces. You build reusable, configuration-driven computation components, enforce strong controls and guardrails over automated output, and guarantee that every result reconciles and can be reproduced and audited. You provide technical leadership for the backend team and partner with data, AI, and product to translate domain logic into robust, reusable services. What makes this role different The output of this layer sits next to externally reported financials, so correctness is not negotiable. Everything you build is held to a hard standard: every result must be traceable to source, reproducible, and auditable , with clear guardrails that keep verifiable results separate from anything that requires human review — and nothing is ever quietly fudged to make the math look complete. • 8+ years of backend engineering in Python, Go, Java, or similar, including significant time at Staff+ level owning complex, data-intensive systems. • Production Python with strong service design and testing practices. • Elite SQL and data modeling. You’re fluent in window functions and large-scale joins, and you reason rigorously about both correctness and performance. • Set-based, reproducible computation over a data warehouse such as Snowflake or PostgreSQL. • Configuration-driven architecture. You’ve designed extensible services or rules engines where new use cases ship as configuration, not new code paths. • Strong system design for reliability, isolation, and reproducibility. • A correctness mindset. You treat “every result is traceable, explainable, and reproducible” as a hard requirement. • Technical leadership. You set patterns, review designs, and mentor engineers. Nice-to-Haves • Finance, accounting, or analytics domain experience , or genuine curiosity about how financial statements are built and reconciled. • dbt alongside Snowflake or PostgreSQL. • Multi-tenant or customer-hosted (on-prem) deployments , ideally across AWS, Azure, and GCP. • Docker, Kubernetes, and Terraform. • Audit logging, data lineage, and feature-flag or controls systems. • Auditable systems in regulated environments such as fintech, banking, or healthcare. • Exposure to ML, statistical workflows, or LLM application patterns , enough to integrate them well.