Top Data Management Jobs

The role involves driving strategy through data analytics, leading a team, implementing process improvements, and developing data models. Requires strong analytical, problem-solving, and leadership skills.

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

As a Manager in Data Governance at PwC, you will lead data governance strategies, supervise teams, manage client accounts, analyze data for decision-making, and ensure compliance with standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead and grow a data engineering team that designs, builds, tests, and operates large-scale data pipelines and warehouses. Partner cross-functionally to translate requirements, ensure data quality, observability, SLAs, security, and governance, and drive automation and performance for analytics, ML, and product use cases.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

The Data Manager will lead the Data team, manage execution across analytics and engineering, track KPIs, and ensure quality data practices while collaborating with cross-functional teams.

The Senior Manager, Data Analysis will validate business impacting analytical tools, assess methodologies, coach junior staff, and execute validation projects.

Lead and grow a data engineering team to build and maintain data models, batch and streaming ETL pipelines, and governance practices. Partner with platform, product, and business stakeholders to ensure reliable, governed, and scalable data for BI, ML, and operations while driving AI strategy and technical direction.

The Manager will lead payments governance and oversight initiatives, implement data reporting frameworks, and enhance risk management practices through stakeholder collaboration and data insights.

Lead a team evaluating new and existing data assets, standardize due-diligence and analytical methodologies, validate data quality and business value, partner with Product and Technology to support onboarding and OneTru cataloging, establish governance and lineage standards, and coach team members to drive data-driven product and revenue decisions.

Lead and grow a 15-person engineering team to deliver enterprise data, analytics, and automation initiatives. Own end-to-end delivery, technical direction, architecture reviews, and modernization across ETL, data integration, warehousing, and cloud platforms while ensuring regulatory compliance, quality, and operational excellence. Partner with stakeholders to prioritize roadmaps and drive Agile practices, observability, and automation.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

The Senior Manager will lead cross-functional data analysis projects, manage teams, validate tools, and mentor junior staff while enhancing technical skills.

Lead the migration domain to ensure accurate, complete, repeatable migration of customer, account, product, and transactional data. Oversee migration automation/tooling and legacy data hygiene. Own analytical frameworks, data mapping, and reconciliation controls to ensure institutional and regulatory confidence. Lead cross-functional teams and drive product outcomes aligned with business and technology objectives.

Lead a team of analysts to mine and analyze large datasets, build and validate models, improve reporting and operational efficiency, and drive analytics strategy across business units. Collaborate on data storage and BI solutions, implement tests and process improvements, mentor junior staff, and deliver data governance and quality practices.

Lead a team to operate and scale CarGurus' data platform across ingestion, orchestration, storage, and observability. Own roadmap, SLAs, cost, security, and quality controls; improve developer experience; collaborate with product and stakeholders to prioritize initiatives, resolve incidents, and drive platform reliability and scalability.

The Senior Manager of Data Engineering will build a data platform, lead a team, and ensure data accessibility to support AI and business operations.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

Lead Finance Data & Analytics to build trusted data sources, governance, and analytics for Finance and cross-functional stakeholders. Manage analysts, drive data strategy and Snowflake data models, produce insights via SQL and Tableau, and deploy AI solutions to optimize finance decision-making and operations.

Lead and scale the Data Operations team to design, build, and maintain data pipelines and models powering reporting and analytics. Collaborate with stakeholders to deliver data solutions, conduct ad-hoc analyses, champion engineering best practices, mentor data engineers, and improve data architecture, tooling, and governance.

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.