Insight Global

Data Protection Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Protection Engineer with a 12-month contract, paying $70-$75/hr, remote (EST hours). Requires experience in Data Insight (Veritas), Office 365 security, data classification, DQL, and managing data protection in large environments.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
600
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🗓️ - Date
October 21, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Data Management #Security #Classification
Role description
Title: Identity Data Protection Engineer Location: Remote (Must work during EST time zone) Duration: 12 month contract with extentions Pay range: $70-$75/hr Must-Have Qualifications: • Experience with Data Insight (Veritas) , Office 365 security controls and Purview • Strong communication skills to effectively interact with the Office 365 admin team and privacy team. • Expertise in data classification, identifying data, and analyzing data. • Ability to create reports, define policies, and run queries. • Proficiency in DQL. • Knowledge of conditional access policies, data tagging, labeling, and Purview. • Proven track record in managing data protection in large-scale environments. Overview: A pharmaceutical company is seeking a dedicated Identity Data Protection Engineer to lead our data expansion project, rolling out in a larger capacity. This role involves working closely with the Legal, Privacy, and Office 365 teams. Responsibilities: Data Insight (Veritas) - Utility (1/3 of the time): • Create reports and define policies using Data Insight (Veritas) version 7.1.2. • Run queries and work within the consult. • Collaborate with the Office 365 team. • Utilize DQL extensively. Office 365 Security (2/3 of the time): • Manage and enhance existing security controls for Office 365. • Implement conditional access policies. • Perform data tagging and labeling. • Utilize Purview for data management. • Create auto labels and develop policies. • Classify data, including patient data. • Demonstrate a proven track record in large-scale environments.