Comrise

Data Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer on a contract basis, focusing on data management and governance. Key skills include Data Engineering, Automation, and Business Intelligence. Experience with Marketing Analytics is essential. The position is hybrid with a competitive pay rate.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
480
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πŸ—“οΈ - Date
March 5, 2026
πŸ•’ - Duration
Unknown
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
New York, NY
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🧠 - Skills detailed
#Dataflow #Data Processing #Strategy #Automation #Data Engineering #BI (Business Intelligence)
Role description
Job Summary: β€’ In this role, you will partner with Marketing, Strategy, and IT stakeholders to improve how we use data. β€’ You will own the creation, management and improvement of our various data assets, including pipelines, tables, dashboards, and other data products. β€’ You will also help to raise our standards for governance across data platforms and ensure alignment with institutional best practices. β€’ Your work will equip our teams with the data needed to make better decisions contributing to Company’s mission to End Cancer for Life. Job Responsibilities: β€’ Assist with the creation and ongoing management of data tables for use in various data applications. β€’ Collaborate with IT partners to complete data table creation and management work. β€’ Work with Analytics, IT, and Marketing stakeholders to build, optimize, scale, and manage the Business Intelligence ecosystem. β€’ Collaborate with internal stakeholders and agency or vendor partners to ensure alignment with Marketing Analytics perspectives. β€’ Champion a hybrid Service and Self-Service analytics model within Marketing and Communication. β€’ Educate peers while building analytics solutions to support self-service capabilities. β€’ Manage workspaces, data refresh processes, and dataflow creation. β€’ Improve data processing efficiency including automation where possible. β€’ Improve taxonomy, processes, and technical infrastructure that support organizational data work. β€’ Ensure alignment with institutional standards and governance best practices.