Data Engineer

Fourth is no ordinary product software company. We pride ourselves on unique cloud-based SaaS used by several million users which enables hotels, restaurants, bars, and pubs to improve efficiency and save money whether they're buying stock, scheduling staff, or creating recipes.

As Data Engineer at Fourth, you are going to take part in the development of our Azure cloud-based Data and MLOps solution which follows the best standards in the world.

The main responsibility of the data engineer will be to maintain and develop the data and modelling environments and to integrate new data sources to it.

The Data Engineer will be part of a newly formed Data Engineering team, part of the broader Software Engineering department. Your daily job will include: · Writing and improving data pipelines which load data in our data lake and transform it to the Feature store · Ensuring all data is available in the right format and reliable quality. Working on improvements of the quality of data as well as challenges as data shift and concept drift · Testing Data pipelines at staging environments and deploying them at production · Writing proprietary packages / frameworks to be used for internal purposes to make the standard tasks easier (such as data load, model testing, exploratory data analysis, etc.) · In collaboration with ML Engineers and Data Scientists taking part in A/B testing and monitoring solutions Experience and Competencies required · Hands-on experience (2+ years) with building data-powered solutions · Hands-on experience with development, deployment and monitoring of data and machine learning solutions in production · Very strong coding skills (clean and commented code, version control, documentation), experience with the database administration (design, etl, query optimisation) and development skills (dev/uat/prod environments, automated testing and deployment). · Hands-on experience with data lakes (Azure Data Lake, Snowflake, BigQuery, and similar), feature stores (SQL-like databases) – design of new features, adding new data sources from API and other services, database design, query optimisation, distributed computing, performance optimisation · Hands-on experience with DataOps and MLOps – namely development, testing, deployment and monitoring of data and ML solutions; using tools like MLFlow, KubeFlow, AirFlow or similar; Git and Docker · Hands-on experience with feature engineering and data pipelines using Python, SQL and similar tools · Experience with larger data sets and production environments · Interest in data science, machine learning, and forecasting is a big plus

Benefits and Culture:

· 25+ days off, as well as birthday day off and 4 charity days off per year· Flexible start and end of the working day and hybrid working mode, including a combination remote and in the office · Personal career development plan, learning paths and mentorship· Team-centric atmosphere· Competitive salary package · Encouraging healthy lifestyle and work-life balance including supplemental health insurance· New parents bonus scheme