Description :

We are looking for passionate backend engineers to help us build and scale these mission-critical systems that drive decisions across pricing, profit, product placement, and customer satisfaction. This role sits at the intersection of backend engineering, data platforms, and applied machine learning transforming experimental models into reliable, production-ready systems that operate at scale. Engineers in this space will not only solve complex technical challenges but also directly shape the way Wayfair competes in the global marketplace through smarter pricing and profitability strategies.

Responsibilities :

- Collaborate with Data Scientists and fellow Software Engineers to bring ML models into production.

- Design, build, and maintain backend services and data pipelines that enable data-driven decision-making.

- Write efficient, scalable, and maintainable code in Python, with a focus on high reliability and performance.

- Develop and optimize Google BigQuery (GBQ)/SQL queries, including joins and aggregations across large datasets.

- Ensure production readiness through testing, monitoring, deployment, documentation, and proactive troubleshooting.

- Use orchestration tools (e. g., Cloud Composer) to schedule and manage workflows.

- Support report generation and integrations with tools like Looker Studio or similar.

- Take ownership of projects end-to-end, from design and implementation to scaling and ongoing

maintenance.

- Balance the open-ended nature of science-driven experiments with the rigor of production-grade engineering.

Requirements :

- 6- 9 years of experience in backend software engineering with strong Python development skills.

- Experience with SQL/GBQ, including writing queries and handling large-scale data.

- Proven experience building and maintaining production systems with a focus on reliability.

- A strong sense of ownership, curiosity, and a continuous improvement mindset.

- Excellent problem-solving and collaboration skills across technical and non-technical partners.

Nice to Have :

- Experience working with Data Scientists to productionize ML models.

- Familiarity with orchestration software (e. g., Cloud Composer, Airflow).

- Experience with monitoring and alerting tools (e. g., DataDog, Prometheus).

- Exposure to reporting/visualization tools (e. g., Looker Studio, Tableau).