Backend Engineer (JSON Schema Developer)

Job Summary

The Backend Engineer (JSON Schema Developer) reviews dense, unstructured PDF documentation and architects comprehensive JSON extraction schemas from scratch. Core daily duties include engineering deeply nested structures, embedding field-level validation logic (such as conditional arrays and type constraints) directly into JSON schemas, defining API contracts, writing clear field documentation, and collaborating on data architecture standards to ensure AI models can reliably generate structured data.

Foundational Schema Engineering & Architectural Prerequisites

• Schema Mastery: Proven, hands-on experience in data modeling, relational or non-relational database design, and advanced schema development (ideally using JSON Schema Draft 7, 2019-09, or 2020-12 rules).
• Deep Nesting Competence: Advanced proficiency in designing highly complex, deeply nested JSON structures from completely blank pages, mapping complex data hierarchies smoothly.
• Unstructured Analysis: Strong architectural thinking with a proven ability to analyze dense, messy, and unformatted documents (such as 150-page financial statements, legal contracts, or manuals) and translate them into well-organized information models.
• Validation Integration: Expertise in embedding functional constraints, data validation keywords, precise field typing, and logical expressions (e.g., regex pattern matching, enum constraints, minimum/maximum values) directly within schemas.
• Structural Integrity Focus: Deep conceptual understanding of required vs. optional fields, type selection parameters, array item configurations, and field-level documentation best practices.
• Communication Articulation: Exceptional written and verbal English communication skills, with a proven ability to explain complex technical data architecture choices clearly to remote team members.
• Distributed Independence: Track record of operating successfully and independently within a distributed, remote-first development framework.

Preferred Technical Multipliers

• API Contract Design: Experience designing formal API contracts (OpenAPI/Swagger, gRPC, or GraphQL) and collaborating on multi-system integration initiatives.
• High-Variance Mapping: A strong background in extracting, cleaning, and structuring data from highly variable, inconsistent, or unstructured corporate documents.
• Scalable Information Modeling: Practical familiarity with data architecture and scalable information modeling paradigms in fast-paced product environments.

Key Responsibilities

1. Complex Document Deconstruction & Data Modeling (35%)

• Analyze Unstructured Assets: Review and thoroughly analyze highly complex PDF documents ranging from 25 to 150 pages (including complex financial balance sheets, logistics records, and technical specs) to catch and map vital data points.
• Translate Hierarchies: Convert loose, unstructured prose, tables, and lists into well-organized, highly normalized, and logical hierarchical data models.

2. High-Level JSON Schema Architecture & Validation (35%)

• Engineer Schemas from Scratch: Design and write comprehensive, zero-fault JSON extraction schemas from completely blank pages, determining the best field naming conventions, explicit data types, and logical structures.
• Embed Advanced Logic: Build field-level validation mechanics directly within the JSON schemas, using syntax rules to enforce deep typing, regex patterns, null-testing, and strict formatting requirements.
• Structural Nesting Management: Configure highly complex nested objects, repetitive array matrices, and conditional data blocks to safely handle fluctuating source data volumes.

3. API Contract Engineering & Technical Documentation (20%)

• Define API Contracts: Define and write clean API contracts, matching data properties perfectly to ensure smooth integration loops across backend systems.
• Write Clean System Specs: Document field definitions, required vs. optional settings, and validation rules clearly, providing human-readable descriptions that keep schemas understandable and maintainable over time.

4. Collaborative Quality Control & Best Practices (10%)

• Standardize Data Layouts: Champion data architecture best practices across the team, driving absolute consistency, clean reusability, and scalability across our schema repositories.
• Coordinate Requirements: Work with cross-functional stakeholders to clarify ambiguous data rules, refine schema boundaries, and improve technical design decisio

Skills Required:

• Computer / Software / It / Data