Principal Backend Engineer

Company Details data² is an explainable AI SaaS company building reView, a graph-based decision intelligence platform purpose-built for highly regulated industries - energy, defense and intelligence, financial services, and supply chain. Our mission is to make AI trustworthy, transparent, and deployable at enterprise scale, with verifiable answers rather than plausible-but-wrong ones. We operate as a cross-border team spanning the United States (Bellevue, WA), Canada (Alberta), and Mexico City - where our technical hub (Kinesis Tech MX) drives platform engineering and product development. The Mexico City team works directly on the core reView platform alongside our US and Canadian engineering, product, and commercial leadership. Job Description reView is a distributed graph-native analytics and reasoning platform built on a microservices architecture. At its core is a semantic execution and verification system that transforms ambiguous analytical questions into explainable, governed graph computations. This role focuses on building backend systems that preserve semantic correctness across ingestion workflows, graph execution, distributed services, and analytical reasoning paths. In our platform, correctness is not just whether an API returns a response. Correctness means: graph relationships resolve to the intended entities, traversals preserve the meaning of the underlying data, derived computations remain explainable and reproducible, distributed workflows maintain consistency under load and failure, analytical results are verifiably correct rather than merely plausible. This is a backend and systems engineering role centered on graph execution, semantic reasoning infrastructure, distributed workflows, and correctness-oriented platform architecture. The role is best suited for engineers who enjoy distributed systems, graph execution, query semantics, and correctness-oriented platform design. Scope Backend and systems-focused engineering role Design and evolution of semantic execution, graph validation, and reasoning infrastructure Close collaboration with platform, ingestion, and graph engineering teams Containerized local development and shared staging environments for integration and execution validation Leveling: At the mid level, you will implement and extend core platform behaviors and correctness mechanisms. At the senior level, you will shape execution semantics, system architecture, and how correctness is enforced across the platform. Requirements Semantic Execution & Backend Systems Design and implement backend services for graph execution and reasoning workflows Build and optimize graph traversal, query planning, and computation behaviors over connected datasets Develop validation and regression coverage for critical execution paths and service boundaries Contribute to execution semantics, identity resolution, and consistency guarantees across distributed workflows Execution & Workflow Validation Test distributed behavior under retries, partial failures, and asynchronous execution Ensure consistency and reproducibility across services and graph workflows Data & Graph Validation Verify correctness and consistency of node and relationship creation in Neo4j / Memgraph Design mechanisms that preserve identity, traversal correctness, and semantic consistency across ingestion and execution flows Define and evolve graph test fixture strategies, including data seeding, isolation, and repeatability Performance & Reliability Run recurring load and stress tests against ingestion, graph execution, and query workflows Identify and resolve bottlenecks across APIs, graph queries, and distributed execution paths Collaborate with engineers on scaling behavior in Kubernetes environments Must-Haves 5+ Years Experience working in Kubernetes or distributed systems 5+ Years Experience building production backend systems in Python 5+ Years Experience with FastAPI or similar Python frameworks 3+ Years Experience designing or debugging asynchronous or distributed execution workflows Strong written and spoken English skills for cross-border collaboration Nice to Haves Familiarity with graph databases (Neo4j, Memgraph, JanusGraph, etc.) Familiarity with LLM or agentic systems Experience with query planning, execution engines, compiler/interpreter design, or type systems Experience building data-intensive or analytics-heavy backend platforms Familiarity with graph query languages and execution concepts (Cypher, traversal planning, query optimization, execution pipelines) #J-18808-Ljbffr