Top Data Management Jobs

Manages DoD data, records, configuration, and change-management processes. Maintains CDRLs, equipment inventories, records plans, databases, and SharePoint environments; supports audits, data requirements, reporting, compliance, and database administration. Collaborates with government staff and contractors, provides user support, develops management briefings, and ensures cybersecurity and information-security practices are followed.

Leads data operations professionals supporting an overhead imagery processing program. Oversees the preprocessing pipeline from satellite data acquisition through ingestion and storage, manages technical projects and teams, maintains government customer relationships, and ensures complex data pipelines operate reliably at scale. Requires experience with quantitative analysis, ETL, SQL/NoSQL databases, geospatial datasets, cloud architectures, and active TS/SCI clearance.

Leads enterprise data architecture strategy and governance for Citi’s fraud domain. Responsibilities include developing data models, flows, standards, and remediation frameworks; architecting scalable data solutions; supporting analytics and AI use cases; partnering with senior stakeholders; and managing data architecture analysts. The role requires extensive financial services experience, fraud-domain expertise, enterprise data architecture capabilities, executive communication, and cross-functional leadership.

Manages advancement CRM systems, data integrity, fundraising and financial reporting, prospect research, donor analytics, and staff CRM support. Identifies and qualifies major-donor prospects, maintains constituent intelligence, and supports portfolio and moves management. Coordinates advancement services, alumni and donor communications, web updates, and special projects while collaborating with development leadership, gift officers, marketing, IT, and university departments.

The role involves leading the data governance team, managing IT projects, collaborating with stakeholders, and ensuring continuous improvement of processes. Responsibilities include strategic planning, client management, and optimizing resource utilization, while overseeing governance activities and departmental management.

Leads compliance risk data analytics and testing strategy, plans and executes analytical projects, evaluates analytical tools, prototypes emerging practices, and translates technical findings for legal, compliance, risk, and executive stakeholders. The role also includes cross-functional partnership, written and verbal communication, and coaching and mentoring junior staff.

Leads and develops a technical analytics team supporting customer solutions, marketing, digital channels, products, services, voice of the customer, and contact center operations. Manages analytics delivery, team capacity, governance, quality, technical standards, and stakeholder partnerships. Translates complex findings into actionable recommendations and ensures solutions are reliable, scalable, adopted, and aligned with business outcomes. Builds analytics capabilities, coaches talent, and drives continuous improvement across platforms, architecture, data quality, and technical roadmaps.

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.

Leads and grows a data engineering team, owning ingestion and pipeline architecture, technical roadmaps, delivery, team performance, and engineering rigor. Partners with analytics, data modeling, product, and business leaders to prioritize initiatives and ensure reliable, analytics-ready data. Provides architectural guidance across Azure Data Factory, Snowflake, API and file ingestion, while managing capacity, technical debt, incident response, and stakeholder communication.

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.

Manage a team of 2–3 analysts while remaining hands-on with SQL, dbt models, dashboards, and complex operational analyses. Lead ambiguous, cross-functional initiatives, deliver recommendations to senior stakeholders, ensure data quality, and improve reporting automation. Partner on AI analytics strategy, data readiness, agent evaluation, and human-in-the-loop governance. Responsibilities include coaching, prioritization, performance management, technical review, KPI development, and operational insights across customer experience, fraud, payments, workforce management, and VIP functions.

Manage data governance practices, data quality, metadata, catalogs, lineage, taxonomies, and data management processes. Partner with business stakeholders to define standards, resolve data issues, assess system changes, and improve data integrity, security, privacy, and usability. Lead project plans, analyze data quality, support remediation, and provide strategic direction while managing multiple initiatives and collaborating across teams.

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.

Leads a small team responsible for enterprise data engineering, data warehousing, database administration, integrations, SharePoint, Nintex automation, Power BI enablement, governance, security, and operational reliability. The role owns Microsoft Fabric and EDW architecture, builds and supports ETL/ELT pipelines, manages platform roadmaps and vendors, applies AI-enabled automation, and partners with IT and business stakeholders to deliver trusted, secure, governed data services.

Build and lead Crown Castle’s Data and AI Governance Engineering capability. Establish governance, data quality, metadata, lineage, access, observability, and AI governance strategies while automating controls and workflows across Databricks and the broader data ecosystem. Remain hands-on with prototyping, testing, troubleshooting, and reusable engineering patterns. Partner with technical, legal, security, risk, audit, architecture, and business teams to improve data trust, accountability, discoverability, and responsible AI adoption.

Leads data engineering, business intelligence, and analytics teams; oversees data pipelines, integration infrastructure, database architecture, CI/CD practices, and enterprise data platform strategy. Partners with business and technology stakeholders to define requirements, improve data capabilities, maintain Ubuntu-based services, and deliver reporting and KPIs to leadership. Responsible for team hiring, development, mentoring, operational health, and technical execution across analytics and data integration platforms.

Manages and advances an enterprise data governance program, including governance standards, stewardship, metadata, definitions, lineage, semantic models, data quality, and reporting consistency. Leads governance committees, resolves cross-functional data issues, supports data literacy, and embeds governance into analytics, reporting, systems, and business processes. Partners with technical and business stakeholders, supervises staff, and drives accountability across the organization.

Generate actionable insights from large and diverse datasets using statistical and machine learning methods. Develop predictive models, segmentation approaches, data pipelines, and features to support health programme targeting and evaluation. Design and analyze behavioural experiments, translate technical findings into clear narratives, and collaborate with internal teams and external partners to inform policy and programme decisions.

Leads market intelligence for global data center and power generation markets. Monitors industry and competitor trends, develops forecasts, conducts market analyses, manages intelligence resources and external vendors, and translates complex information into strategic recommendations. Partners with business leaders on investment, portfolio, growth, and long-term planning decisions while supporting strategic projects and mentoring analysts.

Develops and scales forecasting models, tools, and analytical processes using complex pharmaceutical datasets. Designs, codes, and prototypes analytical logic; synthesizes claims, EMR, sales, CRM, market research, and competitive data; ensures data quality and compliance; and visualizes model outputs. Partners with product, data science, engineering, and business teams to implement machine learning and statistical methods, translate business questions into recommendations, and influence senior stakeholder decisions.

Leads hands-on enterprise data engineering and architecture across Microsoft Fabric and Azure. Designs and optimizes data warehouses, pipelines, integrations, data models, BI solutions, DevOps practices, security, reliability, observability, and data quality standards. Troubleshoots complex production issues, guides AI enablement and Power BI capabilities, conducts technical reviews, and mentors engineers. The role requires advanced SQL, T-SQL, Python, PySpark, Data Vault, dimensional modeling, cloud data platform expertise, and technical leadership.

Leads marketing data analysis, audience segmentation, campaign targeting, and performance insights using SAS, SQL, Python, Databricks, and Salesforce Data Cloud. Translates marketing strategies into campaign specifications, develops and activates customer audiences, validates targeting logic, and communicates recommendations to marketing, business, compliance, and technology stakeholders. The role improves targeting methodologies, supports campaign execution, identifies growth opportunities, and provides specialized business insights while ensuring governance and regulatory alignment.

Manages academic and institutional data collection, validation, analysis, compliance reporting, accreditation submissions, rankings, benchmarking, and dashboards. Coordinates reporting for ABA, NECHE, IPEDS, U.S. News, and other regulatory requirements. Maintains data standards, dictionaries, calendars, and supporting documentation while collaborating across academic, administrative, and technology departments. Provides analytical support for strategic planning, institutional effectiveness, governance, and continuous improvement initiatives.

Analyze medical claims (inpatient, outpatient, SNF, rehab, professional, DME) to identify utilization and root causes. Pull/integrate data, perform QA, present findings, maintain attribution methodology, coordinate stakeholders, and develop tools. Primary coding: Hive, SQL, Python.