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Strong global brand, popular data-engineer title, mid-level scope and hybrid/metro location increase applicant competition.
Core data engineering skills are transferable, though SAP/ERP and specific platform experience add moderate domain specificity.
Multiple required technical platforms and tool familiarity raise selectivity despite no explicit years requirement.
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Develop structured data sets and data models to support business analytics including management, operational, predictive, and data science capabilities.
Design, develop, and support scalable ETL packages and data migration across multiple databases and ERP systems like MS Dynamics, Oracle, and SAP.
Participate in testing, validation, and documentation of data transformations and model designs ensuring accuracy and alignment with project needs.
Bachelor's degree or equivalent in computer science, software engineering, information technology, or related field.
Moderate level experience in data engineering and data mining in a fast-paced environment; familiarity with big data tools (Hadoop, Cassandra, Storm) and multiple databases (SAP, SQL, MySQL, Microsoft SQL).
Experience with programming/scripting languages such as Perl, Bash, Shell Scripting, Python; and knowledge of Microsoft Azure Data Factory, SQL Analysis Server, SAP Data Services, SAP BTP.
Workplace type: Hybrid working; Relevant certifications like SAP, Microsoft Azure, Certified Data Engineer preferred but not mandatory.
Comfortable working with complex multi-terabyte scale data analytics solutions and building secure, scalable system architectures.
Experienced in automation and scripting with proven implementation success, with moderate proficiency in languages including .NET.
Capable of translating business requirements into data models and ETL solutions while collaborating across organizational levels and stakeholders.