Big Data Engineer

Experience

• Hands-on Big Data experience using common open-source components (Hadoop, Hive, Spark, Presto, NiFi, MinIO, K8S, Kafka).
  
  
• Experience in stakeholder management in heterogeneous business/technology organizations.
  
  
• Experience in banking or financial business, with handling sensitive data across regions.
  
  
• Experience in large data migration projects with on-prem Data Lakes.
  
  
• Hands-on experience in integrating Data Science Workbench platforms (e.g., KNIME, Cloudera, Dataiku).
  
  
• Track record in Agile project management and methods (e.g., Scrum, SAFe).
  
  

Skills

• Knowledge of reference architectures, especially concerning integrated, data-driven landscapes and solutions.
  
  
• Expert SQL skills, preferably in mixed environments (i.e., classic DWH and distributed).
  
  
• Working automation and troubleshooting experience in Python using Jupyter Notebooks or common IDEs.
• Data preparation for reporting/analytics and visualization tools (e.g., Tableau, Power BI or Python-based).
  
  
• Applying a data quality framework within the architecture.
  
  

Role description

• Datasets and data pipelines preparation, support for Business, data troubleshooting.
  
  
• Closely collaborate with the Data & Analytics Program Management and stakeholders to co-design Enterprise Data Strategy and Common Data Model.
  
  
• Implementation and promotion of Data Platform, transformative data processes, and services.
  
  
• Develop data pipelines and structures for Data Scientists, testing such to ensure that they are fit for use.
  
  
• Maintain and model JSON-based schemas and metadata to re-use it across the organization (with central tools).
  
  
• Resolving and troubleshooting data-related issues and queries.
  
  
• Covering all processes from enterprise reporting to data science (incl.
  
  ML Ops).