Back End Developer

Company Description:
Golden Gate Innovations delivers advanced AI-driven technology solutions for high-growth startups across Silicon Valley. The company focuses on turning innovative concepts into scalable, high-performance products using AI, engineering simulations, intelligent automation, and cybersecurity engineering. Its core capabilities include AI and machine learning, optimized simulation-based modeling, secure product development, robust cloud and web infrastructure, and modern security architectures. By combining deep technical expertise with a product-first mindset, Golden Gate Innovations helps startups accelerate innovation, strengthen security, improve efficiency, and gain a lasting competitive edge in a rapidly evolving technology landscape.
About the Role:
We are seeking an experienced Backend Engineer with strong data engineering experience to join our engineering team. The ideal candidate will have strong expertise in the Node.js/TypeScript ecosystem, backend API development, databases, and data-intensive systems.
You will be responsible for designing and building scalable backend services, data processing pipelines, ETL workflows, and APIs that work with large and continuously growing datasets. The role requires a strong understanding of backend architecture, database performance, data transformation, reliability, and scalable data processing.
This is a backend-focused role with significant exposure to big data, ETL pipelines, geospatial data, analytics, and distributed data processing.
Key Responsibilities
Design, develop, and maintain scalable backend services using Node.js, TypeScript, and Express.js.
Build robust RESTful APIs and backend services for data-intensive applications.
Design and implement ETL/ELT pipelines for ingesting, transforming, validating, and loading large datasets.
Work with large structured and semi-structured datasets from APIs, files, and third-party data providers.
Develop batch and streaming data processing workflows with appropriate error handling, retries, monitoring, and recovery mechanisms.
Work extensively with PostgreSQL, PostGIS, MongoDB, Redis, and modern data platforms such as Databricks, Snowflake, or similar data warehousing and data lake technologies.
Design efficient database schemas, indexes, queries, and data access patterns for high-volume workloads.
Optimize backend systems for low latency, high throughput, scalability, and reliability.
Work with geospatial datasets and spatial queries where applicable.
Build services that expose processed and aggregated data efficiently to frontend applications and other consumers.
Design data workflows capable of handling datasets ranging from millions to potentially billions of records.
Implement data validation, quality checks, deduplication, transformation, and normalization processes.
Integrate and manage data from multiple external APIs, vendors, and public data sources.
Implement authentication, authorization, rate limiting, caching, and other backend security mechanisms.
Monitor, troubleshoot, and debug backend services and data pipelines in production.
Write clean, maintainable, testable, and well-documented code.
Participate in architecture discussions, code reviews, and technical decision-making.
Collaborate with frontend engineers, data/ML engineers, product managers, and other stakeholders.
Continuously evaluate technologies and engineering practices that improve backend and data-processing capabilities.
Required Qualifications
2–4 years of professional experience in backend or software engineering.
Strong proficiency in JavaScript/TypeScript and Node.js.
Strong experience building production-grade backend services and RESTful APIs.
Strong understanding of backend architecture, asynchronous programming, concurrency, and distributed systems fundamentals.
Hands-on experience designing and implementing ETL/ELT pipelines and data workflows.
Strong experience with PostgreSQL and/or MongoDB.
Good understanding of SQL, database design, indexing, query optimization, and transaction management.
Experience working with large datasets and data-intensive applications.
Experience with batch processing, data transformation, ingestion, and data validation.
Understanding of caching, queues, background jobs, retries, and failure recovery.
Experience integrating third-party APIs and external data sources.
Understanding of authentication and authorization mechanisms such as JWT and OAuth.
Strong understanding of software engineering principles, clean code, and maintainable architecture.
Experience with Git and modern software development workflows.
Knowledge of at least one major cloud platform such as AWS, Azure, or GCP.
Strong problem-solving and debugging skills.
Strong communication skills and the ability to work independently and take ownership of backend systems.
Preferred Qualifications
Experience with Apache Kafka, Apache Airflow, Dagster, or similar data-processing/orchestration technologies.
Experience working with Apache Spark, or similar data-processing technologies.
Experience with distributed systems and high-throughput data processing.
Familiarity with data warehousing and data lake concepts.
Experience with Redis and caching strategies.
Experience designing and maintaining CI/CD pipelines.
Experience with cloud object storage such as AWS/GCP/Azure.
Familiarity with vector databases, embeddings, or AI/RAG systems.
Familiarity with GraphQL or WebSockets.
Experience with automated testing using Jest, Mocha, Chai, or similar frameworks.
Experience with observability tools for logging, metrics, tracing, and application monitoring.
Technical SkillsBackend
Node.js
TypeScript / JavaScript
Express.js
REST APIs
Microservices
WebSockets
Asynchronous processing
Background jobs and queues
Databases & Storage
PostgreSQL
MongoDB
Redis
SQL
Database indexing and query optimization
Object storage such as Amazon S3
Data Engineering
ETL / ELT pipeline design
Data ingestion
Data transformation and normalization
Batch processing
Streaming data processing
Data validation and quality checks
Large-scale dataset processing
Kafka / Airflow / Dagster or similar technologies
Cloud & DevOps
AWS / Azure / GCP
Docker
CI/CD
Application monitoring
Logging and observability
Distributed systems fundamentals
AI & Data
RAG
Embeddings
AI-powered applications
Vector search
Data pipelines supporting AI/ML applications

Work arrangement
No