Top Data Management Jobs

Leads data analysis and data product initiatives involving large databases, modeling, testing, reporting, process improvements, and product analytics. Manages and mentors junior analysts, partners with technology and business teams, supports data-system design, and develops strategic tests for products and marketing materials. Requires strong analytical, programming, business intelligence, communication, and leadership skills.

Establishes and improves data governance controls, risk assessments, and control documentation across the firm. Partners with Chief Data Office stakeholders, lines of business, control teams, and leadership to identify risks, remediate gaps, maintain regulatory alignment, and improve data quality, governance, reporting, and control consistency. Serves as a subject matter expert on data governance policies, standards, processes, and controls within the CORE platform.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

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.

Lead data analytics strategy, mine and analyze large-scale datasets, build and validate models, improve reporting and operational processes, evaluate products and marketing initiatives, and consult on data systems. Manage and mentor junior analysts, collaborate across business units, oversee concurrent projects, and apply business intelligence tools to support customer experience and organizational decision-making.

Leads data engineering and analytics engineering teams delivering production pipelines, curated data products, semantic models, dashboards, and governed Databricks lakehouse capabilities. Owns platform administration, data quality, observability, security, cost optimization, reliability, and engineering standards. Manages internal and external resources, mentors engineers, drives AI-assisted development, partners with stakeholders, and reports roadmap progress to leadership. Requires strong hands-on Databricks expertise, technical leadership, advanced SQL, data modeling, CI/CD, and up to 5% travel.

Drive data strategy and governance for Revenue Operations by building scalable customer and firmographic data infrastructure, improving data quality, implementing AI-powered automation, and establishing governance frameworks. Partner with Sales, IT, Data, Intelligence, Analytics, and CRM stakeholders to optimize Salesforce and external data integrations, develop playbooks, manage data ownership, resolve issues, and lead change management across complex initiatives.

Leads the Data Engineering team and sets technical direction for the company’s data platform. Owns Snowflake, PostgreSQL, pipelines, modern ELT tooling, Medallion architecture, reliability, scalability, cost optimization, data quality, observability, governance, and security. Partners across Engineering, Analytics, Product, Finance, Risk, and Marketing to deliver reusable data solutions. Develops engineers, establishes standards, communicates technical tradeoffs, and builds a long-term platform roadmap supporting analytics, automation, and AI.

Leads data analysis and analytics for Capital One’s card account data products. Responsibilities include mining large databases, validating data models, improving processes and reporting, evaluating products and marketing initiatives, consulting on data systems, and collaborating across business units. The role also manages and mentors junior analysts, supports strategic product decisions, and contributes to business intelligence and project delivery.

Leads enterprise data analysis and analytical tool validation initiatives for card data products. Responsibilities include developing validation frameworks, planning validation projects, assessing methodology quality and risk, researching emerging analytical tools, prototyping best practices, preparing reports and presentations, and coaching junior staff. The role requires collaboration with data product and technology partners, independent problem solving, and management of concurrent projects.

Leads Customer Success analytics strategy, executive business reviews, customer experience and voice-of-customer insights, enablement impact measurement, and data foundation development. Manages two analysts, partners with data engineering and data science, applies statistical and experimental methods, analyzes structured and unstructured customer data, and uses AI tooling to improve self-service and recurring analytics. Translates ambiguous business questions into actionable recommendations for senior leaders.

Leads a team of data engineers and subject matter experts delivering cloud-based data pipelines, AI-enabled workflow automation, and operational maintenance for HEDIS programs. Oversees cloud migration, regulatory reporting, quality measure compliance, stakeholder and auditor communications, technical architecture, production readiness, and service-level commitments. Mentors team members, resolves cross-functional dependencies, evaluates enterprise AI tools, and ensures scalable, reliable data engineering delivery across healthcare insurance data domains.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Responsibilities include distributed data engineering, data modeling, warehousing, streaming, security controls, and full-stack solution delivery using Python, Spark, Databricks, Snowflake, and databases. The role influences developers, analysts, and data scientists; collaborates with product and engineering teams; establishes reusable design patterns; communicates technical outcomes; and mentors data professionals.

Lead enterprise data risk analytics by defining and standardizing metrics, building dashboards and data products, monitoring trends, and delivering actionable insights to executives. Translate data policies into measurement frameworks, automate metric processes with AI, lead cross-functional initiatives, and mentor junior analysts. The role requires substantial data analysis, programming, business intelligence, stakeholder influence, and strategic data management experience.

Leads Enterprise Supplier Management analytics strategy, data product development, analytical tool validation, and data ecosystem improvements. Partners with senior leaders, Product, Technology, Risk, and Operations teams to evaluate business models, manage strategic priorities, assess analytical tool risk, and develop insights. Oversees and mentors a team of at least four Data Analysts while communicating findings through validation reports and presentations.

Lead and grow an enterprise data engineering team while owning the Snowflake and Airflow platform roadmap. Build reliable, governed data products and modernize pipelines using AI-assisted and agentic workflows. Establish standards for quality, observability, lineage, cost efficiency, incident response, and documentation while partnering with Analytics, Data Science, Product, and business stakeholders.

Leads and grows a data platform engineering team responsible for high-volume financial data ingestion, Databricks lakehouse architecture, canonical financial models, orchestration, streaming, reliability, and infrastructure. Drives migrations from legacy pipelines, establishes data quality and platform standards, optimizes performance and costs, and partners with product, accounting, ML, and infrastructure teams to deliver scalable, auditable financial systems.

Manages data risk for Capital One’s Risk Management organization by ensuring compliance with enterprise data standards, developing governance strategies and processes, monitoring data management activities, identifying remediation needs, and reporting gaps to leadership. The role provides guidance, influences enterprise policies, collaborates with stakeholders, supports change management, and addresses risks involving complex data systems and usages.

Designs, builds, and supports scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, distributed processing, lakehouse architecture, data warehousing, security controls, observability, and real-time data solutions. The role collaborates with product and engineering teams, influences technical standards, communicates data outcomes, and mentors data professionals while delivering resilient solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Leads business and system requirements gathering, stakeholder solutioning, documentation, configuration, troubleshooting, quality assurance, testing, and release-note coordination. The role supports customization requests, reviews configurations, oversees project testing, monitors junior business analysts, and helps deliver solutions for fintech and financial-services clients.

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 data governance and risk programs for Global Payment Networks. Analyze large datasets, build and validate models, improve data and reporting processes, establish controls, support privacy and regulatory compliance, and lead cross-functional projects. The role also includes mentoring junior analysts, managing concurrent initiatives, and consulting on data systems, product testing, and business intelligence solutions.

Leads enterprise compliance testing analytics, tooling strategy, and analytical projects. Partners with engineering, data science, legal, compliance, risk, and executive stakeholders to identify and mitigate compliance risks. Evaluates analytical tools, researches emerging practices, communicates findings through reports and presentations, and mentors junior staff. The role requires advanced data analysis, statistical or econometric modeling, large-scale data manipulation, data governance, and people management experience.