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Data Engineering Principal

2 months ago

London, Greater London, United Kingdom Metyis AG Full time
About the Role

We are seeking a highly experienced Data Engineering Principal to join our team at Metyis AG. As a key member of our Data Engineering Team, you will be responsible for leading the design and execution of data engineering projects, while supporting our clients in executing their data strategies.

Key Responsibilities
  • Project Leadership: Independently lead and manage the design and execution of data engineering projects, ensuring timely delivery and high-quality results.
  • Client Support: Support our clients in executing their data strategies by designing and building operational data platforms, including ETL pipelines, data anonymization pipelines, data lakes, near real-time streaming data hubs, web services, and machine learning model operationalization.
  • Technical Solutions: Engineer complete technical solutions to solve concrete business challenges in the areas of digital marketing, e-commerce, business intelligence, and self-service analytics.
  • Quality Assurance: Troubleshoot and QA work done by team members, ensuring data engineering best practices are followed.
  • Collaboration: Collaborate closely with partners, strategy consultants, and data scientists in a flat and agile organization where personal initiative is highly valued.
  • Data Governance: Advise clients on the implementation of effective data governance processes.
  • Knowledge Sharing: Lead knowledge sharing sessions with peers, client prospects, and clients.
  • Client Communication: Communicate and interact with clients at the executive level.
  • Team Guidance: Guide and mentor team members.
Requirements
  • Education: BSc or MSc in Computer Science, IT, or relevant fields desirable.
  • Experience: 8+ years of professional experience in the Data Engineering domain.
  • Language Skills: Excellent command of both verbal and written English.
  • Data Analysis: The ability to draw inferences from complex and diverse data.
  • Data Architecture: Experience designing and setting up modern data lake architectures.
  • SQL: Proven experience in SQL.
  • Version Control: Experience using Version Control (e.g. Git), Testing, and CI/CD.
  • Data Quality: Experience in the automation of data quality monitoring.
  • Cloud Platforms: Experience of creating data workflows on one of the leading cloud platforms – Azure, AWS, or GCP.
  • Data Governance: Knowledge of good data governance practices across the data lifecycle.