- 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

Preferred candidate profile :

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 Springboot 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