Who We Are
Our partner is a multi-brand platform for fashion and lifestyle and we encourage our users to click what they love, read what they want and only buy what they need. They hand-pick better buys and connect people with hot products and brands via inspiring content — from sustainable options and pre-loved pieces to life-long investments. Just the good stuff.
This goes hand in hand with their sustainability focus, and their research into brands and passion for finding the very best products reflects this. Having an inherent drive and an unfolding commitment to sustainability, they're developing at lightning speed.
We hope you do too!
Intro to Role
Your mission is to understand business processes and how to translate them into a data flow, pulling data from different sources into Azure data lake and then enabling advanced analytics on top of that data.
Doing so, you'll be mastering constant improvement and keep it simple, two values that form the foundation of our Engineering Team. Besides this being a great way to meet like-minded people and get inspired!
What are your responsibilities?
- Understand fashion and eCommerce and how that translates into a data model
- Understand content and user interactions and how that translates to a data model
- Ability to choose from state of the art technologies to design data-solutions
- Have experience with technologies and frameworks around event processing, GraphQL & REST APIs, extracting data from website and ETL processes
- Help design web and mobile event tagging and build pipelines to enable Power BI reports
- Develop pipelines to source, clean and transform data, be responsible for data quality, automate e2e pipeline, manage and automate the infrastructure and deployment, etc.
- You implement solutions hands on as an engineer or work with other internal and external professionals to achieve a best practice data-environment
- You talk with product managers and our internal users to understand their needs to adapt which data we capture and how
- Enable data processing, distribution and analytics to be able to recommend the right products to our users
- Be a team player
- Live a culture of innovation, passion, and unrelentingly high standards
- Work in a rapid, and effective way
- 7+ years in a data engineering / data architect role, preferably with experience working on fashion-related products in an agile environment
- Experience developing and orchestrating complex data pipelines in both batch and stream fashion, on-boarding data from different source system, data cleaning and transformation.
- You understand the challenge of designing and development of large scale distributed system
- Understand the best practice and common design pattern of big data processing
- Passionate about big data and machine learning technology. Rich experience in big data technology, especially Spark and the Hadoop ecosystem
- Excellent programming skills in Python. Not only familiar with language itself, but also familiar with best practices, code structure, design patterns, development workflow etc
- Hands on experience in DevOps and automate software development process, like familiar with tools like Jenkins, Docker and Kubernetes, etc
- Experienced in one of cloud solution like AWS, Azure or GCP would be a big plus
- A good team player in an agile team, you are willing to take different kinds of tasks to meet sprint commitment
- You enjoy working with people, and have a track record for gathering data requirements and designing solutions in a dynamic environment
- Results-oriented - you understand the business goals and metrics and try to achieve the best outcome within given constraints
- Pragmatic - applying a strong, positive attitude, you overcome challenges with passion and creativity
- Good communication abilities - you are a natural at consensus-building and decision-making
- You inspire others and are good at what you do, but remain curious and feel that you have much left to learn
- You are proficient in English
Product Knowledge / Technical Skills
- Azure Data Lakes, Azure Data Factory
- ETL pipelines
- Spark, Databricks
- Google Analytics
- Power BI
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