We are currently recruiting a Data Engineer for an internal retail data science and analytics squad that use machine learning and big data to help marketing, product, finance and logistics managers make smarter decisions. This is a highly commercial team and should appeal to any experienced data engineers who love building out Proof-of-Concept models and data solutions as well as deploying them in production.
Projects you work on could involve insights, data & ML pipelines, serving data via APIs, running models in Spark or building analytical tools and engines for business decision-makers.If you were in this team over the last 6 months, here are some of the things you would have done:
Minimum Requirements and Experience:
- Worked closely with data scientists, senior analytics leaders and non-technical business managers on 1 x 6 month project or perhaps 2 x 3 month analytics POCs.
- You are the expert on data platforms, data pipelines, helping data scientists build more robust solutions and all things related to getting the right data in the right place in the ideal format.
- You will have shared your personal opinion on any new, exciting technologies and data tools and are comfortable presenting ideas in informal and formal settings to analytics teams as well as marketing, finance and executive stakeholders.
- Helped train data scientists on best practice with; Containers (docker and kubernetes), CI/CD, using Git and version control, using APIs
- You may have also contributed to team knowledge-sharing, presenting ideas on an open-source project or new and emerging technology that could make life easier for your peers!
- Must have worked in a commercial data engineering role, closely with data scientists for a minimum of 3-4 years to help them build models in POC AND Production environments
- Machine Learning Engineering skills will be looked upon favourably
- Excellent communication skills and confidence presenting ideas and work to non-technical stakeholders if required
- You are a natural team player and have an active interest in learning more from other specialists, such as data scientists, machine learning engineers and business leaders
- Python, SQL, Spark and cloud experience; AWS, GCP or Azure