Full Stack Developer — Automotive Retail Catalog Platform

Overview

About the Role

We are looking for a Full Stack Developer to join our Catalogue Search Technology team building and scaling our automotive parts catalog platform on Google Cloud. You will own features end-to-end — from React-based search experiences to Java microservices powering product resolution, fitment matching, and catalog ingestion pipelines. This role sits at the intersection of modern cloud engineering, data-intensive retail systems, and emerging GenAI capabilities, Vertex Retails API for Commerce.

What You'll Do

· Design, develop, and maintain React.js front-end applications for product catalog search, fitment lookup, and merchandising tools

· Build and evolve Java/Spring Boot microservices for product resolution, catalog ingestion, and pricing APIs

· Develop and optimize Cloud ETL pipelines on GCP (Dataflow, BigQuery, Cloud Functions, Pub/Sub) for large-scale product data processing (1.2M+ SKUs, millions of fitment records)

· Integrate with Google Vertex AI Retail Search API for product catalog indexing, search, and recommendations

· Implement observability practices — create dashboards (Grafana, Cloud Monitoring), alerts (PagerDuty, ServiceNow), and SLO-based monitoring for production services

· Apply GenAI/LLM capabilities to improve catalog data quality, product matching, and search relevance

· Participate in CI/CD pipeline management, container orchestration (GKE/Kubernetes), and infrastructure-as-code (Terraform)

· Collaborate with Product, Data Engineering, and Merchandising teams to translate business requirements into technical solutions

Must-Have Qualifications

Strong in at least one track (front-end OR back-end), competent in the other:

Front-End Track

· 4+ years with React.js (hooks, context, Redux/Zustand, TypeScript)

· Experience building responsive, accessible SPAs with modern tooling (Vite/Webpack, Jest, React Testing Library)

· REST/GraphQL API consumption, state management, and performance optimization

Back-End Track

· 4+ years with Java 11+ and Spring Boot microservices

· RESTful API design, Hibernate/JPA, messaging (Kafka/Pub/Sub), and relational + NoSQL databases

· Experience with distributed systems patterns: circuit breakers, retry, rate limiting, caching

Both Tracks

· 6+ years total software development experience

· Solid understanding of DevOps fundamentals — CI/CD (GitHub Actions, Jenkins, Cloud Build), Docker, Kubernetes basics, GitOps workflows

· Working knowledge of GCP services — Cloud Run, GKE, BigQuery, Cloud Storage, Dataflow, Pub/Sub

· Experience with ETL/data pipeline design — batch and streaming ingestion, data transformation, schema evolution

· Hands-on experience building observability — structured logging, metrics, distributed tracing, alerting dashboards (Grafana, Cloud Monitoring, Datadog)

· Understanding of Generative AI concepts — LLM integration, prompt engineering, RAG patterns, vector search, or AI-assisted development workflows

Good-to-Have Qualifications

· Automotive retail / parts catalog domain knowledge — ACES/PIES data standards, fitment data structures, base vehicle/engine base mapping, part terminology

· Experience with Google Vertex AI Retail Search API or similar product catalog search platforms (Elasticsearch)

· Familiarity with high-cardinality data modeling — attribute bucketing, product variant hierarchies (PRIMARY/VARIANT), multi-value attribute indexing

· Experience with Terraform for infrastructure provisioning on GCP

· Knowledge of ServiceNow integration for incident management workflows

· Exposure to BigQuery ML or Vertex AI for catalog enrichment / product classification

· Performance engineering — profiling, load testing (k6, Gatling), query optimization

Tech Stack

Layer Technologies

Front-End React.js, TypeScript, Redux, Material UI, Vite

Back-End Java 17, Spring Boot, Spring Cloud, Hibernate

Data / ETL BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Functions, Kafka

Search Google Vertex Retail Search API, Elasticsearch

Cloud GCP — GKE, Cloud Run, Cloud Storage, IAM, VPC

DevOps GitHub Actions, Cloud Build, Docker, Kubernetes, Terraform

Observability Grafana, Cloud Monitoring, Cloud Logging, PagerDuty, ServiceNow

GenAI Vertex AI, Gemini API, LangChain, RAG pipelines

What We Value

· Ownership mindset — you ship features, monitor them in production, and fix what breaks

· Pragmatic engineering — right-sized solutions over over-engineering

· Data fluency — comfort working with large datasets, complex schemas, and pipeline debugging

· Curiosity about GenAI — actively exploring how LLMs can improve developer productivity and product experiences

· Clear communication — ability to explain technical trade-offs to non-technical stakeh