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Data Platform lead - SAS

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Onsite

Data Platform lead - SAS

Shrive Technologies LLC

Data Platform lead - SAS

1 month ago

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Important information


Contract type:

Permanent

Salary:

$55 – $60 / hour

Location:

Somerville, NJ 08876, USA

Work mode:

Onsite

Published on:

21 October 2026

What they need


Provide expert-level support, maintenance, and troubleshooting for statistical analysis software platforms (e.g., SAS, R, SPSS, Python-based environments). - Monitor platform performance, ensure high availability, and proactively resolve incidents and technical issues. - Collaborate with cross-functional teams to implement software updates, patches, and enhancements while minimizing downtime. - Assist in the integration of statistical analysis tools with enterprise data sources and reporting solutions. - Develop and execute platform activity reports, usage analytics, and capacity planning documentation. - Ensure compliance with organizational security policies, license management, and audit requirements. - Coordinate with vendors and technology partners for issue resolution and service improvement. - Provide technical guidance and mentorship to junior team members and contribute to knowledge-sharing initiatives. SKILL REQUIREMENTS - 5+ years of experience supporting and administering statistical analysis software platforms in an enterprise environment. - Proficiency in SAS, R, SPSS, or equivalent statistical software, with a strong understanding of installation, configuration, and maintenance procedures. - Experience with scripting languages (e.g., Python, Shell, PowerShell) for automation and platform management. - Solid grasp of Linux/UNIX and Windows operating systems. - Strong problem-solving, analytical, and troubleshooting skills. - Excellent communication and documentation abilities. - Relevant certifications in statistical software platforms or IT infrastructure (e.g., SAS Certified Platform Administrator, ITIL). - Experience with cloud-based analytics platforms or containerization technologies (e.g., AWS, Azure, Docker). - Familiarity with data security standards and compliance frameworks. - Background in supporting large-scale analytics or data science projects.

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