Top Entry Level Data Management Jobs

Manages data operations for approximately 25 perinatal clinical research studies. Responsibilities include designing research databases, maintaining data dictionaries and documentation, extracting and cleaning complex datasets, developing dashboards and monitoring systems, conducting analyses, ensuring data quality and regulatory compliance, supporting reporting and publications, training research staff, and improving EHR, AI, and data-management workflows.

Leads master data governance, quality, lifecycle management, and operational improvement for Innovative Medicine Quality Management and Digital. Defines MDM strategy, standards, ownership, controls, metrics, and workflows across product, supplier, equipment, organization, and reference data. Partners with business and technology stakeholders to resolve data issues, support compliance and audits, improve integrations, and drive adoption of governance practices. Mentors data stewards and influences enterprise-wide data quality and management initiatives.

Leads master data management for Innovative Medicine Quality Management and Digital. Defines governance strategies, standards, ownership, quality metrics, and lifecycle processes across product, supplier, equipment, organization, and reference data. Oversees cleansing, integration, monitoring, compliance, auditability, and continuous improvement. Partners with business and technology stakeholders, chairs governance forums, resolves data issues, drives adoption, and mentors data stewards and subject matter experts.

The Data and BI Manager leads reporting and analytics design, development, implementation, and infrastructure management. Responsibilities include maintaining dashboards and data warehouses, scaling reporting performance, guiding deployment, auditing financial data accuracy, and supervising related activities. The role requires advanced SQL, BI expertise, data programming knowledge, communication skills, and systems leadership experience.

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.

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.

Leads logistics analytics and the Infor Nexus supply chain visibility platform. Owns platform configuration, reporting, backlog prioritization, agile delivery, data quality, governance, and stakeholder requirements. Oversees Power BI dashboards, semantic models, logistics KPI reporting, and EDW data partnerships. Manages analysts and matrixed IT and vendor resources while improving shipment visibility, carrier performance insights, ETA accuracy, and decision-making.

Lead the data engineering team and oversee the DBA team while driving GCP-based streaming and batch data platform initiatives. The role combines people leadership with hands-on work across BigQuery, dbt, Apache Flink or Dataflow, data quality, testing, observability, governance, and cost optimization. The manager will promote AI-assisted engineering practices, ensure transactional database reliability, and align data infrastructure with business and product objectives.

Leads the data engineering squad and oversees the DBA team in a GCP-native environment. Drives streaming and batch platform initiatives using BigQuery, dbt, Apache Flink, and Dataflow; promotes AI-enabled engineering operations; ensures database reliability, data quality, testing, observability, governance, documentation, and cost optimization; and aligns data platforms with business and product goals.

Build and maintain research data infrastructure connecting laboratory instruments, analysis pipelines, LIMS, repositories, databases, and cloud platforms. Develop ingestion and data transformation pipelines, manage metadata and persistent identifiers, deploy Kubernetes-based services, maintain CI/CD and infrastructure-as-code, and integrate AI/ML tools for data discovery. The role also evaluates data-management technologies, trains research staff, and produces technical documentation and procedures.

Lead enterprise data platform architecture and client implementations centered on Databricks and lakehouse technologies. Design data models, governance, security, scalable pipelines, CI/CD, observability, and cost-management strategies. Develop and optimize batch and streaming workloads using Spark, PySpark, Scala, and SQL across cloud and hybrid databases. Mentor data engineering teams, review architectures and code, resolve complex platform issues, and collaborate with business, security, infrastructure, BI, and ML stakeholders to deliver reliable data products.

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 a data engineering team delivering secure, scalable data platforms, pipelines, semantic layers, and analytics products. Oversees architecture, governance, data quality, observability, CI/CD, stakeholder alignment, delivery planning, and engineering standards. Develops technical talent, drives modernization and AI/ML readiness, supports cloud and platform evolution, and ensures trusted data assets meet enterprise reporting and decision-making needs.

Downloads, verifies, compiles, and analyzes datasets to support CDMRP data operations. Validates data quality and completeness, documents data pulls, creates visual process maps, and tracks incoming requests and deliverables. Requires basic SQL skills and proficiency with Visio, Excel, and Access, along with U.S. citizenship and eligibility for NACI and ADP III designation.

Lead and mentor data engineering and DBA teams while driving GCP-based streaming and batch data platform initiatives. Oversee BigQuery, dbt, Apache Flink or Dataflow, data quality, testing, observability, governance, reliability, and cloud cost optimization. Remain hands-on with coding, code reviews, architecture, and complex system problems while aligning data infrastructure with business and product goals. Champion AI-assisted engineering and automation across the platform.

Lead the data engineering squad and oversee the DBA team while driving GCP-based streaming and batch infrastructure projects. Build and review code, establish standards for data quality, testing, observability, governance, and cost optimization, and align data platforms with business goals. The role requires expertise in GCP, BigQuery, dbt, Apache Flink or Dataflow, relational databases, AI-assisted engineering, and team leadership.

Lead the data engineering squad and oversee the DBA team while remaining hands-on with platform projects. Drive streaming and batch infrastructure using GCP, BigQuery, dbt, and Apache Flink or Dataflow. Establish standards for data quality, testing, observability, governance, documentation, and cloud cost optimization. Promote AI-native engineering practices, ensure transactional database reliability, mentor engineers, and align data platforms with business and product goals.

Manages wildfire debris-removal data, including field-data quality assurance, load-ticket reconciliation, invoice discrepancy identification, reporting, and project-document control. The role coordinates reconciled and voided tickets with contractors, prepares payment recommendations, exports client reports, maintains project records, and supports closeout activities. Work occurs in remote Alaska with extended shifts, weekends, and potentially 70–90 hours per week during the response period.

Leads OpenAI’s Growth & Revenue data engineering team, owning data strategy and delivery across growth, subscriptions, billing, payments, monetization, and revenue. Builds and manages a high-performing team, develops reliable and well-modeled data products, establishes trusted metrics and quality standards, and partners with Data Science, Product, Finance, and Engineering. The role also oversees roadmap planning, privacy, governance, observability, incident response, stakeholder communication, and team hiring and development.

Manage and curate clinical, biospecimen, experimental, raw, and derived scientific data for a biomedical research laboratory. Maintain institutional and cloud data resources and scientific websites, support open science and FAIR principles, contribute to grants and manuscripts, train lab members, and attend scientific conferences. The role requires careful data management, collaboration with researchers and engineering staff, strong communication, and the ability to manage shifting priorities in an in-person academic environment.

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 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.

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.