Data Engineer

2 days ago

Kuala Lumpur, Kuala Lumpur, Malaysia Getronics Full time 80,000 - 160,000 per year

Job Description - Data Engineer

Summary

Responsible for building and maintaining our data infrastructure including pipelines and data storage.

Roles & responsibilities

  • Design, develop, and maintain data pipelines to ingest data from SAP ECC, SAP S4 Hana, and SAP.
  • Collaborate with data architects and other stakeholders to build efficient data integration solutions.
  • Ensure data quality and integrity by implementing robust data validation and testing processes.
  • Optimize data storage and retrieval processes using Azure Data Lake Storage and Synapse.
  • Develop and maintain ETL processes using Azure Data Factory and Databricks.
  • Monitor and troubleshoot data pipelines to ensure smooth and reliable data flow.
  • Work closely with analytics and business intelligence teams to understand data requirements and deliver data solutions that meet their needs.

Experience & profile

  • Master or bachelor degree in Computer Science, Statistics, Mathematics, Artificial Intelligence, Physics or related technical discipline or equivalent combination of education, training and experience.
  • Proven experience of three to five years as a Data Engineer, with a focus on ingesting data from SAP ECC, SAP S4 Hana, and SAP BW.
  • Strong background in Azure cloud technologies, including Azure Data Factory, Databricks, Azure Data Lake Storage, and Synapse.
  • Proficiency in SQL and experience with data modeling and database design.
  • Familiarity with data validation and testing processes to ensure data quality.
  • Excellent problem-solving skills and the ability to troubleshoot complex data issues.
  • Strong communication and collaboration skills to work effectively with cross-functional teams.
  • A proactive and self-motivated approach to learning and staying current with industry trends and best practices.
  • Experience with Agile project management methodology.
  • Signs of strong performance
  • High business value from delivered products
  • Low mean time to repair
  • Infrastructure cost lower or equal to plan
  • High NPS of team (if applicable)
  • High mean time between failures
  • Reaches high % of SLOs
  • Strong product match with business needs

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