Senior Data and Backend Engineer
Urban, United Kingdom
Job Description
The Role
At Urban, data driven decision making is key to the operation of our marketplace business, enabling us to effectively match customer demand and practitioner supply. This role will have ownership of our data infrastructure, providing hands-on maintenance of the data pipeline to ensure it addresses the needs of our evolving business. This role will sit within the Engineering team and collaborate with cross-functional teams, including a data analyst, to ensure the efficient and up to date flow of data throughout the company. Being champion in the data domain, this role would also actively contribute to initiatives requiring backend development, collaborating with other senior backend engineers.
As a product team, we are highly-skilled multi-disciplined engineers, passionate about quality of our product, about the experience all of our users will have using our product. We work closely with each other across design, engineering, and testing. We embrace strong development standards, continuous integration, peer review, test automation, and replicable deployment processes.
Being champion in the data domain, this role would also actively contribute to deliver business requirements across a few dozens of backend microservices, along with Senior Backend Engineers. To evolve our architecture and to keep it up to date with the latest technologies. Will be applying best engineering practices and continue our journey towards engineering excellence.
You will be working as part of small international engineering team distributed across multiple countries and time zones. You will get access to different cultures and opinions and will be releasing changes to the production environment in small chunks, daily.
Requirements
- As the owner of our data infrastructure, actively develop and maintain the Airflow Data Pipeline and Data Warehouse on Google Cloud Platform, including any bug fixes or adapting the pipeline to upstream changes
- Co-own and maintain our dataflow upstream (Segment, Zapier, etc) and our behavioural tracking (Segment, Google Analytics and other analytical products). Behavioural data includes events, sessions and attributions on our platforms ranging from iOS/Android Apps to Website.
- Ensure the quality of data in our data warehouse
- Maintain operations of established data science models (for example: customer propensity scoring models, segmentation models, preference prediction)
- Providing support on our existing Looker’s code base in LookML.
- Collaborate with the business to liaise with vendors on projects and handle ad-hoc data requests from stakeholders.
- Develop and maintain documentation for data infrastructure and data flows.
- Participate in backend projects
- Stay up-to-date with the latest industry trends and technologies in data engineering.
Skills
Required
- Bachelor or higher education background in Science, Maths, Engineering or Computing majors
- 5+ years of experience in data engineering or related field
- Expert in SQL and confidence in different data models or data architectures.
- Expert in Python, especially on Data pipeline and data manipulation.
- Excellent understanding of data engineering, or backend programming best practices.
- Excellent understanding of data modeling and data architecture principles.
- Highly independent, able to critique requirements from internal and external stakeholders before determining the best course of action
- Experienced with NodeJS and Typescript
- Understanding of server-side architecture and RESTful API design
- Strong understanding of data security and governance.
- Strong communication and collaboration skills, with the ability to work effectively in a remote team environment.
Desirable
- Experience in Airflow or other programming data pipeline platform desirable, is preferred
- Experience with cloud-based data storage and computing platforms, such as GCP, AWS, or Azure, is preferred
- Experience in Data Ops or Dev Ops, especially on distributed systems, is preferred
- Experience with productionising data science model, is preferred
- Familiarity with continuous integration and continuous deployment (CI/CD) pipelines
- Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes)
- Understanding of software development principles, design patterns, and best practices