Principal 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 Lead 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. Data Engineers are an integral part of the emerging Data Platform at Fourth which will cater our customers with state of the art analytics, near-real time and forecasting solutions built by top notch Analytics team, software engineering teams and Data Science team. Their role will sit in the heart of it and will require intensive collaboration with the Data Science, Analytics and all Software Engineering teams at Fourth. The main responsibility of the Data Engineer is to build and optimize complex and sophisticated data infrastructure that empowers our Analytics, Near Real Time and Data Science teams and their delivery. Your daily job will include:

· Design and work on implementing Data processing infrastructure for batch and real-time data processing. · Data processing, development, and deployment of end-to-end data engineering solutions in Azure cloud environment. · Implementing solutions for monitoring of data quality and cleaning the data. · Collaboration with ML Engineers, Data Scientists and Analysts for productization of Data Science and Analytics solutions.· Collaboration with Software Engineers on enabling them consume and expose the Data Lake and complex Data Infrastructure to both feed in and consume analytics, near-real time or forecasting data. Experience and Competencies required:

· Hands-on experience (5+ years) 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 ETL tools, 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, data pipelines design, query optimisation, distributed computing, performance optimization. · Hands-on experience with DataOps and MLOps - development, testing, deployment and monitoring of data and ML solutions; Experience with Synapse KubeFlow, AirFlow or similar; Git and Docker · Hands-on experience with feature engineering using Python, PySpark, SQL and/or similar tools · Nice to have: Hands-on experience with Azure based Data infrastructure or similar. 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