Top Data Management Jobs

Leads data analysis strategy for an analysis platform product, including data mining, modeling, testing, reporting, process improvement, and product analytics. Manages junior analysts, mentors team members, collaborates across business units, and supports large-scale databases and data storage systems. The role includes business intelligence, strategic experimentation, operational efficiency improvements, and consulting on production and data system design.

Leads data governance, risk mitigation, analytical tool validation, and program development for Global Payment Networks. Responsibilities include planning validation projects, assessing analytical methodologies and tool risks, researching industry practices, preparing reports and presentations, and coaching junior staff. The role requires managing concurrent projects and applying data analytics, risk management, statistical, and project management expertise to develop enterprise-wide controls and processes.

Lead a team delivering Power Platform and AI-driven analytics solutions (Power BI, Power Apps, Power Automate, Copilot Studio, Snowflake). Guide data modeling, governance, automation, and AI-powered workflows; prioritize initiatives, modernize reporting, and mentor engineers to drive scalable, self-service analytics and business application adoption.

Develop data models and automated solutions supporting semiconductor supply chain mapping, resiliency, distribution operations, and supply forecasting. Perform exploratory and targeted analyses, preprocess structured data, monitor model effectiveness, and present insights through data visualization. Collaborate with technical teams and stakeholders on cross-functional projects, communicate data-driven recommendations, and manage ambiguous business problems independently. The role requires domestic and international travel of less than 25% and follows a hybrid work schedule.

Analyze large-scale data, build and validate models, improve processes and reporting, evaluate products and marketing initiatives, and consult on data systems. The role requires programming, strategic analysis, collaboration across concurrent projects, and leadership in a hybrid environment.

Leads enterprise ontology and data-modeling strategy, governance, integration, standardization, and roadmap development. Manages teams creating semantic models, taxonomies, linked data, and reusable capabilities while partnering with technology, machine learning, and business stakeholders. Advocates W3C standards, supports knowledge organization workshops, establishes modeling guidelines, monitors industry trends, and mentors team members. Requires expertise in metadata, ontologies, linked data tools, graph technologies, SQL or SPARQL, and semantic standards.

Own and maintain complex datasets, data processes, and client-facing dashboards. Partner with account, strategy, operations, and product teams to deliver data products, identify analytics opportunities, and improve business intelligence capabilities. Lead cross-functional project engagements, developing team processes and client deliverables while translating client requirements into effective data solutions.

Own complex datasets, data processes, and client-facing dashboards while delivering advanced analytics and business intelligence capabilities. Partner with account, strategy, operations, and product teams to support client deliverables, improve data products, and identify business opportunities. Lead cross-functional teams and develop scalable processes for successful client engagements.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Leads enterprise analytical tool validation, including framework refinement, project execution, methodology risk assessment, technical research, and prototyping of industry practices. Communicates findings through reports and presentations, partners with cross-functional leaders to influence strategy, and coaches junior staff. The role also manages multiple concurrent projects and evaluates statistical and econometric models, data governance, and predictive analytics approaches.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Leads data analytics for commercial underwriting and portfolio management, using models, large datasets, BI, automation, and API integrations to manage credit risk, improve processes, and support strategic decisions. Develops analytical tools, reporting improvements, validation tests, and scalable data solutions while consulting on production and storage systems. Manages concurrent projects, collaborates across business units, and may lead people and project initiatives.

Designs and optimizes data-driven audience targeting and measurement for marketing channels. Builds reusable audience datasets, measurement views, quality controls, alerts, and anomaly detection to improve campaign execution and reduce operational risk. Partners with marketing, sales, product, and technology teams to define requirements, implement governed artificial intelligence capabilities, quantify automation impact, and communicate actionable insights. Mentors junior analysts and supports scalable, controlled marketing transformation.

Lead a global analytics team to transform financial and operational data into actionable insights. Partner with stakeholders to align analytics with strategy, build scalable Power BI dashboards integrated with Snowflake, apply predictive modeling and ML, enforce data governance, drive automation with Python and Power Automate, and improve data architecture and reporting to inform decision-making and business performance.

Leads a data engineering team responsible for data modeling, batch and streaming ETL pipelines, analytics-ready datasets, data governance, and data quality. Hires, coaches, and manages engineers while setting technical direction and partnering with engineering, product, and business stakeholders. Oversees scalable data foundations across regulatory, customer, business, content, and growth domains, and advances practical AI automation and AI-ready infrastructure.

Leads client-focused data analytics initiatives within PwC’s Risk & Regulatory practice. Develops business intelligence, visualization, and advanced analytics solutions using Python, Oracle Business Intelligence, QlikView, and Spotfire. Builds stakeholder relationships, validates outcomes, drives process improvement, and applies systems thinking to solve complex business challenges. Oversees scalable deployments and coaches teams through ambiguous situations while delivering strategic insights and improved client performance.

Leads data analytics and business intelligence strategy, mining large datasets, building and validating models, improving processes and reporting, evaluating products and initiatives, and consulting on data systems. Manages and mentors junior analysts, collaborates across business units, and oversees projects from inception through completion. The role requires advanced analytical, programming, communication, leadership, and problem-solving skills.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Leads data analytics strategy and project delivery for a global payments network. Mines and analyzes large-scale datasets, develops and validates models, improves processes and reporting, evaluates products and marketing initiatives, and supports new data systems. The role requires collaboration across business units, business intelligence expertise, strategic testing, and management of multiple concurrent projects. It may also involve people management and consulting on data storage and production systems.

Own the full lifecycle of Finance visualization and conversational analytics products, from data modeling and metric validation through deployment and adoption. Build executive dashboards and AI-enabled natural-language experiences using Tableau, ThoughtSpot, and Databricks. Partner with Finance, Product, Data Engineering, and platform teams on data quality, governance, security, compliance, and performance. Manage analytics roadmaps and backlogs, establish delivery standards, mentor analysts, and communicate insights to senior Finance leaders.

The Senior Manager of Data Analysis will manage validation projects, assess analytical tool risks, and mentor junior staff while collaborating cross-functionally and enhancing analytical capabilities.

Lead the Managing Data Steward team for Small Business Bank to define and execute data management strategy, governance, quality controls, and data product development. Drive adoption of open-source and cloud-native (AWS) technologies, oversee data catalog health, monitor KRI's, manage data issue resolution, and report insights using advanced visualization. Coach and develop a high-performing analytics team while partnering with engineering and enterprise functions to standardize data flows and migrate on-prem systems to cloud.

Lead analytics engineering to design and maintain transformation layers, data models, and semantic layers. Ensure data quality, governance, observability, and single source of trusted metrics. Modernize reporting, support self-service analytics, collaborate across teams, review and test code, and leverage AI to improve analytics outputs.

The role involves leading a team to develop AI and ML solutions, manage customer interactions, and analyze large data sets using various technologies.