What we offer

Interact with C-level at our clients on regular basis, to drive their business towards impactful change Lead your team in creating new business solutions Seize opportunities at the client and at Metyis, in our entrepreneurial environment Become part of a fast-growing international and diverse team Step-change your career path by becoming a key player in a significant multi-national consulting firm

What you will do

Independently lead and manage the design and execution of data engineering projects Support our clients in executing their data strategies by designing and building operational data platforms: ETL pipelines, data anonymization pipelines, data lakes, near real-time streaming data hubs, web services, training and scoring machine learning model operationalization Engineer complete technical solutions to solve concrete business challenges in the areas of digital marketing, eCommerce, Business Intelligence and self-service analytics Troubleshoot and QA work done by team members and ensure data engineering best practice Collaborate closely with partners, strategy consultants and data scientists in a flat and agile organization where personal initiative is highly valued Advise clients on the implementation of effective data governance processes Lead knowledge sharing sessions with peers, client prospects and clients Communicate and interact with clients at the executive level Guide and mentor team members

What you’ll bring

BSc or MSc in Computer Science, IT or relevant fields desirable 8+ years of professional experience in the Data Engineering domain Excellent command of both verbal and written English The ability to draw inferences from data that is complex and diverse in nature Experience designing and setting up modern data lake architectures Proven experience in SQL Experience using Version Control (e.g. Git), Testing, and CI/CD Experience in the automation of data quality monitoring Experience of creating data workflows on one of the leading cloud platforms – Azure, AWS, or GCP Knowledge of good data governance practices across the data lifecycle