Senior Backend Product Engineer, Menu Operations

HelloFresh · Toronto, Ontario, Canada · по договорённости

Компания
HelloFresh
Город
Toronto, Ontario, Canada
Зарплата
по договорённости
Уровень
senior
Формат
full_time
Иностранная компания
нанимает русскоязычных

S'more about the team

The Menu Operations team ensures the accuracy, reliability, and consistency of data and workflows across our ecosystem. This role will focus on automated data validation, cross-system reconciliation, monitoring, troubleshooting, and developing scalable solutions that improve data quality and operational efficiency.

The role will work closely with engineering, product, culinary, editorial, and business teams to identify data issues, improve validation processes, and standardize best practices across markets.

As we continue evolving this system, extending its capabilities, and preparing for expansion, we are looking for a Senior Software Engineer with strong backend experience, a talent for turning complex logic into clean, reliable systems, and enough full-stack fluency to support and enhance our internal application.

Above all, we are looking for people who will make HelloFresh better. We believe there are many different ways of developing skills and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you.

Lettuce share what this role will be responsible for

Work beyond your specialization when the problem demands it. Your specialization is your anchor, not your boundary.

Operate what you build. You instrument, monitor, and improve your systems in production. Shipping is the beginning of the learning cycle, not the end.

Design and delivery of scalable backend platforms and distributed systems, leveraging AI-assisted and agentic engineering workflows to accelerate delivery and improve system quality.

Design, develop, and maintain backend services, ensuring scalable, reliable API interactions with our internal application

Collaborate closely with engineers, analysts, and planners to translate domain knowledge into robust, maintainable system logic

Contribute to the evolution of the platform toward event-driven patterns using technologies like Kafka

Support frontend needs for our internal React application, ensuring seamless integration between FE and BE layers

Improve reliability through automated testing, performance tuning, and thoughtful system design

Guide as well as learn through pairing, design discussions, and knowledge sharing to help strengthen collective ownership across a strong, team-first environment

Participate in shaping the future direction of an advanced system

All other duties, as assigned

Sound a-peeling? Here's what we're looking for

Proficiency and production experience with one or more backend languages such as Golang, Python, or Java.

Hands-on experience with Docker, Kubernetes, and modern CI/CD practices in production environments.

Bachelor’s or Master’s degree in Computer Science, Engineering or related field

Hands-on experience building production-grade APIs

Solid knowledge and experience with relational databases and comfortable working with data across various data systems (PostgreSQL, Snowflake, S3)

Experience with event-driven architecture and messaging technologies (Kafka)

Working knowledge of cloud environments, containerization, and orchestration (AWS, Docker, Airflow)

Experience contributing to or designing scalable architecture for internal tools or operational platforms

Full-stack capability with React or another modern FE framework a plus — enough to support and enhance application

Strong communication skills and a proactive, collaborative approach to working with engineers, analysts, planners, and product partners

Comfort working through ambiguity and contributing clarity through simple, pragmatic design choices

A mindset of continuous learning, knowledge sharing, and improving the systems

Hands-on experience working with AI tooling (e.g., Claude Code, Cursor, Copilot) beyond casual experimentation. You use AI agents every day. You have a practical sense of how the context you provide to AI tools shapes output quali

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