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Medium — common data-engineer title with broad big-data/cloud skillset increases applicant competition.
Medium — technical data skills transfer broadly, but domain-specific enterprise and AI-readiness requirements increase specialization.
Medium — multiple required technical skills and cloud/big-data experience but no explicit years or mandatory certifications.
Lead design, development, and maintenance of data and analytics platform projects, focusing on scalable data pipelines and storage solutions.
Implement data governance, monitoring, and troubleshooting for data quality and integrity issues across distributed and cloud platforms.
Partner with cross-functional teams to deliver AI-ready datasets, reusable data assets, and support analytics and GenAI use cases across enterprise domains.
Bachelor’s degree in Computer Science, IT, Engineering, Data Analytics, or equivalent practical experience.
Intermediate professional experience in data engineering including developing enterprise data pipelines and transformations using cloud data platforms and ETL/ELT tools.
Strong skills in SQL, data modeling, data quality validation, and scalable data architecture supporting multiple consumer patterns.
Work Experience Required: Intermediate experience in relevant discipline (data engineering). On-site location with some flexibility (not 100% on-site).
Experienced in building scalable, governed, and discoverable data products using Data-as-a-Product principles aligned to enterprise use cases.
Demonstrated ability to translate business/product requirements into technical solutions balancing quality, performance, and scalability.
Comfortable working in Agile cross-functional teams collaborating with Product Managers, Data Scientists, Architects, and business stakeholders.