Top Entry Level Data Management Jobs in NYC, NY

Manage the integrity, retention, classification, retrieval, documentation, and distribution of NYCT video data. Process daily, legal, depot, and incident-related video requests; maintain system users and security; coordinate technician retrievals when needed; produce reports and support audits. The role also communicates with authorized personnel, explains retrieval procedures, tracks lost video, and recommends process improvements.

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Leads server-side tagging, marketing pixel, and CAPI implementations for clients. Responsibilities include architecture, QA, troubleshooting, UAT, implementation documentation, data quality support, stakeholder training, and coordination with client development teams. Serves as the technical advisor for Tealium, Adobe Experience Platform Tags, Google Tag Manager, and related tracking infrastructure. The role also manages client relationships, supports measurement strategy, and develops one to two direct reports.

Manage the ASISTS enrollment system, enter and maintain student and attendance data, prepare reports for analysis, follow up for outcomes, support intake/registration/testing, assist instructors, develop outreach materials, and complete required trainings.

The Data Manager will develop dashboards, conduct statistical analyses, and support compliance through data management and risk assessments to enhance organizational performance.

Manage and mentor a data engineering team while leading Snowflake-based platform architecture, enterprise data integrations, governance, observability, and pipeline reliability. Partner with analysts, business leaders, product management, and engineering teams to improve trusted self-service data access. Establish engineering standards, modernize the data stack, optimize costs, and deliver an AI-ready data foundation supporting analytics, reporting, and future AI initiatives.