Top Data Management Jobs

Owns governance, quality, integrity, and operational management of contract and customer master data. Oversees contract configuration, pricing controls, member onboarding, eligibility, roster management, exception resolution, metrics, and process documentation. Leads continuous improvement using Lean and Six Sigma methodologies, develops standardized controls, and manages a data operations team while collaborating with Sales, Finance, Reconciliation, IT, and other functions.

Processes, analyzes, and interprets geophysical data, especially electrical resistivity and seismic datasets. Troubleshoots data acquisition equipment, supports survey design and technical report writing, integrates GPS and surveying data, and completes project documentation. The role may include limited fieldwork, requires adherence to OSHA and MSHA safety requirements, and supports project engineers and senior management.

Manages the enterprise Data Governance and Analytics Center of Excellence, leading staff, governance initiatives, analytics strategy, maturity modeling, budgets, vendors, and planning. Coordinates data quality, stewardship, privacy, acquisition, and release programs while partnering with data scientists, engineers, technology governance teams, and business stakeholders. Develops executive presentations, aligns enterprise definitions and algorithms, establishes standards, and ensures analytics projects and business cases meet organizational objectives and deadlines.

Manages business intelligence, data analytics, dashboards, reporting, data governance, and platform architecture for field operations. Extracts and analyzes operational, financial, productivity, and SLA data to identify trends, forecast outcomes, recommend improvements, and support leadership decisions. Oversees data integrity, security, system integrations, workflow configuration, vendor coordination, project delivery, performance measurement, and AI-assisted analytics adoption.

Manage and administer Mission Assurance databases while ensuring data integrity, security, availability, and performance. Support data collection, normalization, governance, quality control, repository integration, stewardship, and analytical workflow sustainment. Integrate assessment data with mission mapping, risk management, and criticality analysis to identify trends, vulnerabilities, dependencies, and enterprise-level risks. Apply advanced analytical techniques, including artificial intelligence and machine learning, to support Air Force mission assurance activities.

Lead advanced data analysis and campaign measurement initiatives, developing statistical models, causal inference methods, dashboards, reporting solutions, and measurement frameworks. Collaborate cross-functionally to evaluate campaign effectiveness, validate incrementality, investigate anomalous results, and provide actionable recommendations. Present complex analytical findings and limitations to executives, influencing strategic and operational decisions. The role requires expertise in SQL, statistical modeling, data visualization, and data science programming languages.

Lead a team to analyze large-scale datasets, build and validate models, improve reporting and operational efficiency, design data/storage systems, run strategic tests, and collaborate across business units to drive analytics strategy and customer experience.

Leads and develops a team of data analysts and BI developers, establishes the analytics strategy and roadmap, prioritizes projects, and oversees dashboards, reports, and data solutions. Partners with business and executive leaders to translate complex problems into actionable insights, while championing data governance, security, team performance, and continuous improvement.

Lead and mentor a team of analysts to deliver HR-related analytics and BI solutions. Mine and model large datasets, design tests, improve reporting and operational efficiencies, and collaborate across business units to inform strategy and product decisions.

Manages Stars data and analytics operations, including projections, data management, vendor performance, IT collaboration, and reporting. Leads departmental planning, staff development, budgeting, workload balancing, continuous improvement, special projects, and complex problem resolution. Supervises professional and non-professional employees while ensuring effective workflows, communication, and delivery of business objectives. Applies healthcare data expertise, analytical skills, and knowledge of HEDIS, CAHPS, HOS, Clinical Rx, and Medicare Advantage Stars programs.

Leads the design, delivery, and reliability of an enterprise data platform. Manages and mentors data engineers, oversees architecture and scalable pipelines, establishes engineering standards, and drives platform modernization. Responsibilities include incident response, monitoring, governance, security, AI-assisted development, automation, and GCP cost optimization. The role requires hands-on technical leadership across data modeling, cloud infrastructure, CI/CD, batch and streaming architectures, and stakeholder collaboration.

Leads data strategy and analytics for the Risk/Credit team, translating complex datasets into insights for Credit, Operations, Marketing, Product, and Finance. Responsibilities include automating Python/R workflows, building Tableau dashboards, partnering with Data Engineering on Databricks workflows, conducting strategic analyses, monitoring data quality, presenting recommendations to leadership, and documenting analytical methodologies. The role requires strong SQL, cloud data warehouse, visualization, analytics programming, distributed computing, and version-control expertise.

Lead a team to design, operate, and scale core data pipelines and analytics infrastructure. Ensure reliable ELT/ETL, dbt-based models, data governance, metric consistency, and cross-functional partnership to deliver trusted self-service analytics and measurable business impact.

Lead a team delivering enterprise-wide model risk reporting and AI-driven automation. Build and deploy scalable ML/AI solutions, conversational reporting, multi-agent workflows, and centralized data integrations. Drive analytics strategy, validate models and data, improve reporting and operational efficiency, and collaborate across business units.

Lead a product analytics team to measure and improve product and platform performance. Mine and analyze large datasets, build and validate models, implement reporting and BI improvements, consult on data/storage system design, run strategic tests, mentor junior analysts, and collaborate cross-functionally to drive data-informed product decisions.

Leads data and analytics consulting teams delivering enterprise data architecture, pipelines, analytical environments, data models, visualizations, and integrations. Responsibilities include hands-on SQL and Python development, project lifecycle leadership, stakeholder workshops, executive communication, risk mitigation, client support, mentorship, recruitment, and practice growth.

Establishes and operates North America Operations’ data-governance framework, including ownership, stewardship, quality, metadata, lineage, definitions, and AI governance. Partners with business, data, technology, risk, and compliance teams to resolve data issues, automate controls, improve trust, and embed governance into data products, analytics, and AI solutions. Develops playbooks, training, metrics, and continuous-improvement processes while driving accountability across functions.

Leads the data platform strategy, infrastructure, operations, and continuous improvement programs. Manages data engineers, provides technical guidance through design and code reviews, oversees data pipelines and incidents, drives observability and automation, coordinates enterprise AI and analytics initiatives, and manages team budgets and schedules. The role also supports data governance, platform technology selection, scalable solution design, and evolving team capabilities.

Lead and scale a data engineering team to build and maintain BigQuery-based ELT pipelines and Medallion architecture. Provide technical direction, hands-on engineering (dbt, Python, Fivetran), enforce governance, data quality, observability, CI/CD and IaC standards, and partner with platform and business domains to deliver a reliable data platform.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Cleans, analyzes, validates, and reports HIV program data from CAREWare, EvaluationWeb, surveillance, testing, prevention, and other sources. Develops data quality checks, resolves inconsistencies, matches datasets, supports reporting and quality management, provides technical assistance to subrecipients, troubleshoots data systems, and translates findings into actionable recommendations for public health leadership and programs.

Design, build, and test SAP MDM repositories and logical data models. Configure MDM clients (Console, Data Manager, Import Manager, Syndication Manager), create import/syndication maps, workflows, web services, DB views, and INI files. Perform repository operations with CLIX, prepare unit tests, validate integrations with upstream/downstream systems, and coordinate with data providers and consumers.

Design, build and maintain SAP MDM repositories and logical data models. Create functional and technical specs, configure MDM clients (console, data/import/syndication managers), web services, CLIX server ops, import/syndication maps, workflows, matching rules, DB views and INI files. Perform unit testing, validation and liaison with data providers.

Implement and configure SAP MDM repositories and clients; design functional/technical specs, import/syndication maps, workflows, web services, CLIX operations, DB views, and test interfaces between MDM and upstream/downstream systems.

Design, build, and validate SAP MDM repositories and logical data models. Configure MDM clients (console, data manager, import/syndication managers), create import/syndication maps, workflows, matching rules, and DB views; perform CLIX and server-level operations, INI configuration, and interface testing. Liaise with data providers, subscribers, and downstream systems and lead the team.