Senior Associate– Data & Analytics Support, Maintenance, and Administration

Senior Associate– Data & Analytics Support, Maintenance, and Administration

Responsibilities

• Oversee day-to-day operation of data warehouses, databases, data lakes, and analytics platforms, ensuring systems are stable, reliable, and performing optimally.
• Manage incident resolution, including investigating, troubleshooting, and resolving data-related issues in a timely manner to minimize operational disruption.
• Continuously monitor performance and optimize queries, ETL pipelines, and other data processes for efficiency and reliability.
• Perform regular system maintenance, including software upgrades, patches, and backups to ensure high availability of data systems.
• Oversee the execution of maintenance and support projects, ensuring timelines are met and resources are used effectively. Manage team activities to support project deadlines and deliverables.
• Develop and refine operational processes, workflows, and best practices for data support and maintenance. Ensure the team follows standardized procedures to ensure consistency and efficiency.
• Ensure all systems, processes, and issues are well-documented, and produce regular reports on system health, incident resolution, team performance, and resource allocation.
• Assist in managing the departmental budget for tools, licenses, infrastructure, and training, ensuring financial efficiency within the department.
• Work with data engineers, business analysts, and IT teams to ensure that support and maintenance activities align with business needs and analytics objectives.
• Manage relationships with third-party service providers, ensuring that vendors meet service level agreements (SLAs) and adhere to agreed-upon delivery timelines.
• Ensure that all data systems comply with relevant security, privacy, regulatory requirements and work closely with security teams to monitor data integrity and safeguard sensitive information.
• Lead the evaluation and adoption of new tools or technologies to improve the efficiency of data analytics systems and enhance team productivity.
• Identify opportunities for continuous improvement in both technical and administrative aspects of data systems. Introduce automation or other process improvements to reduce manual tasks and increase system uptime.

Requirements

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