Principal Backend Engineer

Data Squared USA Inc

$120K — $160K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience in backend systems engineering
  • Proficient in designing backend services for graph execution
  • Experience with graph databases like Neo4j or Memgraph
  • Strong background in distributed systems and semantic reasoning
  • Familiarity with containerized development using Kubernetes

Responsibilities

  • Design backend services for graph execution and reasoning workflows
  • Optimize graph traversal and query planning over connected datasets
  • Develop validation and regression tests for critical execution paths
  • Validate end-to-end platform flows from ingestion to results
  • Test distributed behavior under retries and partial failures
  • Ensure consistency and reproducibility across graph workflows
  • Run load and stress tests to identify and resolve performance bottlenecks

Benefits

  • Remote work flexibility
  • Collaborative work environment with cross-functional teams
  • Opportunities for professional development and growth
  • Emphasis on innovative technology and methods
  • Support for work-life balance through flexible schedules
Full Job Description
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,


  • and 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

  • Validate end-to-end platform flows (ingestion 12 graph 12 query 12 result)


  • 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

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