Data Operations Engineer

• Ensures the reliability, efficiency, and scalability of data pipelines, platforms capabilities, and connected platform tools within ONE DX Data ecosystem.
• Build and manage capabilities allowing decentral data product teams to successfully engineer and deploy data pipelines within the platform.
• Ensure platform capabilities allow for SLA management, total cost of ownership transparency & optimization, end2end data observability and data quality monitoring.
• Ultimately ensure high data availability and reliability for DX business. Work with data engineers, support engineers, application management, data ecosystem architect and data platform product owner to create a lean, high performance, and highly reliant data platform.
• Configure and monitor data platform tools (e.g. dbt, Snowflake, API services, Microsoft analytics tool stack)
• Build robust operations and monitoring system to measure health of pipelines, health of platform and availability of data.
• Translate and implement SLA requirements into new platform capabilities
• Enhance automation in the area of meta data management, testing, data quality, data observability, DevOps integration
• Design and implement platform services/features across the whole stack of data management, i.e. data ingestions (incl. real-time processing), data pipeline management and data automation, data consumption (classical analytics, data as a service, ML/AI usage, data as API service)
• Design and build lean and efficient data security layers (data masking, row access policies, etc.) that can suffice broad requirements from various data domains.
• Manage deployment of decentral development into reliable releases within the data platform allowing for continuous integration and deployment
• Work with platform architect and de-central data product deployment teams to ensure high quality and agile deployments of data pipelines applying state of the art DevOps methods, such as trunk-based-development
• Support data platform architect to continuously improve data platform and operations

Qualifications:

• Broad and certified knowledge of cloud based data platforms (esp. Snowflake and Databricks)
• High proficiency managing, monitoring of interconnected data pipelines (e.g. Airflow, dbt, Snowflake, Databricks, Microsoft Fabric)
• High proficiency in scripting, automation, API services building and management
• Hands on experience in implementing data observability, data quality measurement, logging, alerting, meta data based data contract management
• Broad understanding of data governance and data security
• Practical experience in defining and implementing incident management and support for data platforms
• Practical experience in DevOps CI/CD integration with data platform tools (dbt, Snowflake, Databricks)
• Ability to translate complex problems into simple technical solutions
• Ability to abstract data problems into re-usable components/modules
• Lifetime willingness to learn and explore new technologies in data, analytics and AI
• Strong orientation towards quality and results (attention to detail and standardization)
• Practical experience working in agile environments
• Good communication skills
• Analytical and out of the box thinking
• Natural drive for innovation and optimization
• Teamwork oriented collaborative attitude in a multi-national hybrid team environment

Education/Experience:

• Post degree in computer science, information science, or data analytics
• Expert knowledge and minimum 7 years professional experience on data management and/or data operations in a corporate environment
• Well-versed in Cloud based data management technologies (Snowflake, Databricks, Neo4J, dbt, Airflow, Microsoft Fabric, API management like MuleSoft or Snaplogic)
• Profound knowledge and hands-on experience with building large-scale and reliable data ecosystems
• Proficient English plus an additional foreign language is preferred
• Working experience with Agile methods within Microsoft DevOps
• Experience working with enterprise data in the field of laboratory diagnostics or large global enterprise preferred.
• Experience in working with multinational teams and/or different countries and cultures