The main challenge now is to build a scalable architecture, meaning we have to tackle the challenges that come with using Python for distributed systems; Consistency, availability, service orchestration and much more.
We currently run different services, all with their own CI flows, allowing us to deploy several times a day.
In this role you will join a small, lean team of software engineers and a product manager working on;
- Building the integrated payment and checkout system using Python as well as the latest tools, technologies and methodologies.
- Supporting partner integration teams and ensuring that integration with partner retail checkout systems and API’s is easy and effortless
- Integrations with 3rd party payment systems such as (Stripe, Klarna, Apple Pay etc)
- A secure data management and CRM system for internal and external stakeholders (owasps etc)
- New ways to automate onboarding for retailers that allows for global regulations.
HOW WE WORK
We are developing an environment and culture that is all about writing great software using the best tools in the best possible manner.
We like to build things that solve problems, you will not be constrained by process - we work in small product teams that take a lean approach to building things.
In this team we’re looking for engineers who are excited to work on secure technologies in a python environment. You will ideally have plenty of experience using;
- Python, postgresql, jenkins, circleci, aws, redshift, redis, django, rest framework...
- Docker (http://developers.lyst.com/devops/2014/12/08/docker)
- Microservices architecture (HTTP)
- TDD - We love tests!
- We are using both Kanban and Scrum, depending if team is more customer facing or internal (Jira)
You will be challenged, supported and have the opportunity to learn a lot. You will work a fast paced, autonomous environment with like minded people who are passionate about what they do:
- 50 engineers and data scientists
- 5M duplicated products detected and merged using product image features (http://www.slideshare.net/ejlbell/fashion-productdeduplication)
- 300k online recommendation model updates per day (http://developers.lyst.com/data/2014/11/11/word-embeddings-for-fashion/)
- 72k crowd-sourced labels generated per day
- 40k product gender classifications per day via deep learning
- 500k recommended products per day
- 120 EC2, 8 RDS, 7 ElastiCache and 10 Redshift instances
- our internal analytics system collects ~100M data points/day
...and a team that…
- ~10 deployments/day
- 40+ merged pull requests/day
- 20k lines of change/week
- Lots of open source projects - https://github.com/lyst and https://github.com/SSAW
- Get invited to talk at great events (PyCon, Europython, PyData etc)
- feature toggling and A/B testing