Top Data Management Jobs

The Manager, Data Governance will lead the implementation of data governance strategies to support AI and analytics, ensuring data quality, compliance, and ownership while promoting data literacy across the organization.

The role analyzes large datasets from various platforms to identify trends and inform decisions in artist growth and marketing strategy.

The role requires leadership in enterprise data management, focusing on MDM, governance, data quality, and architecture, ensuring high-quality, trusted data for analytics and operational use, while managing a team and collaborating with stakeholders.

This role involves leading the Data & AI team, building modern data platforms using Microsoft technologies, overseeing project delivery, and mentoring team members while engaging with clients.

The Manager for Data Engineering will lead the Data & AI team, focusing on building modern data platforms, providing mentorship, and overseeing project delivery using Microsoft technologies.

Lead the architecture and delivery of enterprise data platforms, optimize data pipelines, enable AI/ML use cases, engage clients on technical strategy, and develop the data platform team.

Lead enterprise data governance and data quality programs by designing DG policy, metadata/CDEs, lineage, and DQ automation. Build Python pipelines and AI/ML models for automated DQ checks, integrate governance with tooling (catalogs, ServiceNow, dashboards), deliver Power BI/Tableau insights, partner with stewards and SMEs, and monitor DQ KPIs to improve retail, finance, inventory, and omnichannel reporting.

The role involves leading data engineering and analytics teams, optimizing data systems, collaborating on data pipelines, and managing data infrastructure projects.

Design and optimize data pipelines and aggregated reporting tables (Snowflake/GCP). Perform complex analyses on large datasets, write and validate SQL queries, ensure data integrity, and collaborate across enterprise partners to deliver actionable pharmacy-services insights and support reporting, dashboards, and AI-assisted outputs.

Lead development and operation of the enterprise Data & Analytics platform. Own roadmap, platform capabilities, CI/CD, automated provisioning, monitoring, governance, cost optimization, and incident management. Partner with engineering, architecture, security, and business teams, act as product owner for platform services, and manage a team of platform engineers to enable self-service, secure, and scalable data delivery.

Lead architecture and delivery of Radiant's enterprise data platform and AI strategy. Own vendor evaluations for secure AI tooling, mentor engineers, coordinate with software teams, ensure Databricks-based integrations and dashboards meet ITAR/EAR compliance, and act as a hands-on technical leader.

Lead a centrally‑federated team of Data Stewards and Data Quality Analysts to operationalize data governance. Translate policies into stewardship processes, enforce consistent definitions and quality rules, oversee issue escalation and remediation, embed stewardship into projects, monitor KPIs, and partner with governance, engineering, and business stakeholders to maintain metadata, lineage, and data fitness.

Lead and grow a data operations team supporting the cloud market-data platform and upcoming data warehouse. Manage intake, SLAs, pipeline monitoring, data quality, access controls, vendor coordination, and stakeholder communication. Enable datasets for AI, support platform engineers, and oversee deployment, CI/CD, and unstructured data operations.

The Manager of MDM leads projects for Master Data Management systems, working with teams to ensure successful client engagements in the pharmaceutical and life sciences sector.

The Clinical Data Manager oversees data management activities for clinical trials, including designing case report forms, quality assurance, and collaborating with project teams and vendors.

This role involves leading the development and deployment of data engineering solutions, collaborating with different teams, and ensuring data quality and compliance while managing complex data pipelines and workflows.

The Data Specialist will manage BIM data integration and digital ecosystem strategies to support the LA28 Olympic Games, focusing on operational readiness and data-driven decision-making.

The Data Success Manager will source, collect, and manage data for partners, ensure data quality, and support campaign activity, requiring SQL and Python skills.

The Manager, Data Success Management oversees Data Success Managers, manages data operations, supports partners, improves workflows, and ensures data quality.

The Senior Data Manager/Analyst supports data analysis and project designs for foreign assistance programs, ensuring data quality and developing reporting products.

The Data Operations Sr. Manager leads data operations by managing daily workflows, improving data quality, and collaborating with teams to deliver data solutions. Responsibilities include troubleshooting issues, automating processes, and coaching the operations team while ensuring compliance with healthcare data standards.

Lead and manage an analytics team to design and deliver self-service BI, dashboards, reports, and data warehousing solutions. Define analytics roadmaps and metrics, ensure data integrity, collaborate with cross-functional teams, support end users, and manage project communications to drive measurable business results.

Lead and develop data engineering leadership and teams to deliver scalable enterprise data products and platforms. Own product lifecycle, roadmap, and execution; design scalable pipelines and frameworks; enforce data quality, governance, and observability; partner with stakeholders to align data strategy to business outcomes; evaluate and implement modern data technologies; drive adoption and reliability across domains.

Lead a global, hands-on data engineering team building large-scale Databricks/PySpark pipelines, production datasets, customer-facing data feeds, AI-ready analytical datasets, and data quality/observability systems. Partner cross-functionally, use AI coding tools to accelerate delivery, and ensure operational excellence, reliability, and documentation for investor-facing products.

Lead a data engineering team to design, build, and maintain data warehousing solutions and legacy integrations. Oversee architecture decisions, vendor management, Agile delivery, data quality reporting, platform health, stakeholder communication, and adherence to governance and security standards while mentoring engineers and ensuring project execution.