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Mid-level data engineering, metro location, popular skillset and strong global employer drive high competition.
Data engineering skills are broadly transferable, though pharma and SAP familiarity add moderate industry specificity.
Explicit 5+ years requirement and mandatory data engineering experience create rigid filtering.
Design, build, maintain, and optimize enterprise-scale data pipelines and integration workflows supporting reporting, analytics, modelling, and decision-support use cases across the organization.
Ensure data quality, reliability, and timely availability by monitoring, troubleshooting, and improving data engineering processes post-implementation.
Collaborate with stakeholders and cross-functional teams to enable analytics and decision support, maintain documentation, and uphold data engineering delivery standards.
Bachelor's degree in Data Engineering, Computer Science, Information Systems, Engineering, Analytics, or related field required.
5+ years of experience in data engineering, analytics engineering, BI/data platforms, or technology roles.
Strong experience with enterprise-scale data pipelines, data integration, data architecture, data modelling, data quality, metadata management, and governance principles.
Proficiency in cloud data platforms, data lake/warehouse architectures, ETL/ELT frameworks, and analytics enablement technologies.
Experienced in leading data engineering workstreams with effective collaboration across business, analytics, and technology stakeholders.
Familiarity with SAP, SAP BI, Hyperion software, and enterprise data/analytics environments indicating exposure to complex systems.
Capable of managing continuous improvement, technical problem-solving, and performance monitoring within data engineering operations.