Principal Backend Engineer
Data Squared
- Seattle, Washington, United States
- Seattle, Washington, United States
À propos
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)
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Compétences linguistiques
- English
Avis aux utilisateurs
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