Backend Engineer - EG004 - Remote Eligible

Backend Engineer

The Backend Engineer designs, builds, and scales server-side systems, data infrastructure, and integration pipelines that power modern applications across diverse industries including PropTech, AI-powered platforms, SaaS products, and data-intensive startups. This is often a foundational hire where you'll have true ownership in shaping backend architecture, working directly with technical leadership and cross-functional teams to build systems from the ground up. You'll architect scalable cloud infrastructure, implement robust ETL/ELT pipelines ingesting large data streams, manage search and AI integrations, and ensure reliability, performance, and security across all backend systems. This role requires engineers who combine deep technical expertise with systems thinking, building infrastructure that supports real-time product discovery, generative AI experiences, and high-volume data processing.

Your backend architecture will form the foundation enabling applications to scale from initial launch to millions of users without performance degradation. By building robust data pipelines and integration systems, you'll unlock value from diverse data sources, transforming raw data into actionable intelligence. Your infrastructure decisions will directly impact system reliability, cost efficiency, and development velocity across engineering teams. Through thoughtful API design and integration architecture, you'll enable seamless connections between systems, third-party services, and AI capabilities. Your work on search infrastructure and vector databases will power intelligent product discovery and AI-driven experiences. By establishing engineering best practices, monitoring systems, and security protocols, you'll create operational excellence that prevents outages and protects sensitive data.

Core Responsibilities

Backend Architecture & Cloud Infrastructure (30%)

• Design and implement scalable backend architecture supporting high-traffic applications
• Set up and manage cloud infrastructure including VPCs, IAM, security groups, and networking (AWS preferred, GCP/Azure)
• Implement Infrastructure as Code (IaC) using Terraform, Pulumi, or CloudFormation
• Define backend architecture standards, API design patterns, and system integration strategies
• Build cost-optimized cloud strategies balancing performance and infrastructure spend
• Design microservices architecture or monolithic systems based on business requirements
• Implement containerization strategies using Docker and orchestration with Kubernetes
• Establish monitoring, logging, and observability frameworks for system health tracking
• Ensure high availability, disaster recovery, and backup strategies

Server-Side Development & API Design (25%)

• Develop scalable server-side applications using Node.js, TypeScript, Python, or Golang
• Build robust RESTful APIs and GraphQL endpoints with proper error handling and documentation
• Implement authentication, authorization, and security protocols (OAuth, JWT, API keys)
• Optimize backend performance for speed, efficiency, and resource utilization
• Design database schemas and queries for SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases
• Build real-time features using WebSockets, Server-Sent Events, or streaming technologies
• Implement caching strategies using Redis or Memcached for performance optimization
• Write clean, maintainable, well-tested backend code following best practices

Data Integration & ETL/ELT Pipelines (25%)

• Build and scale ETL/ELT pipelines ingesting large data streams from diverse sources (REST, XML, EDI APIs)
• Design resilient, idempotent data loaders that evolve with external data source changes
• Deploy and manage orchestration tools (Airflow, Dagster, Prefect) for workflow automation
• Build and maintain dbt pipelines with automated testing, CI/CD workflows, and version control
• Implement data transformation logic ensuring data quality, validation, and error handling
• Integrate streaming data platforms (Kafka, Kinesis, RabbitMQ) for real-time data processing
• Design schema-flexible pipelines handling varying data structures gracefully
• Manage data warehousing solutions (Snowflake, BigQuery, Redshift) for analytics and reporting

Search, AI & Advanced Integrations (15%)

• Manage search infrastructure including Elasticsearch or OpenSearch clusters for product discovery
• Implement vector databases (Pinecone, Weaviate, Qdrant) supporting AI/ML features
• Support AI integrations through embeddings, RAG (Retrieval-Augmented Generation), and LLM frameworks
• Integrate with AI frameworks (LangChain, LlamaIndex) for intelligent application features
• Build feature stores and model ops infrastructure supporting ML model deployment
• Implement recommendation systems and semantic search capabilities
• Optimize search relevance, ranking algorithms, and query performance
• Support generative AI experiences with proper prompt engineering and context management

DevOps, Testing & System Reliability (5%)

• Implement CI/CD pipelines automating testing, building, and deployment processes
• Establish automated testing frameworks (unit, integration, end-to-end) ensuring code quality
• Monitor system performance using DataDog, Grafana, Prometheus, or CloudWatch
• Implement security best practices including data encryption, secure API design, and vulnerability scanning
• Optimize database performance through query optimization, indexing, and connection pooling
• Manage system scaling strategies (horizontal, vertical) based on load patterns
• Conduct code reviews and maintain engineering documentation
• Ensure data quality, observability, and operational excellence