Top Senior Data Management Jobs

Support regulatory reporting by analyzing trade and customer account data, responding to regulator and customer inquiries, identifying data anomalies, improving reporting processes, implementing AI/ML automation, and ensuring data governance, quality control, and acceptance testing for reporting systems.

The Senior Manager will lead B2B analytics efforts, drive data-driven strategies for marketing, optimize revenue growth, and enhance performance through advanced analytics frameworks and collaboration with stakeholders.

Lead strategy, architecture, and team oversight for the institution's clinical research data warehouse. Align data assets with research and institutional goals, govern common data models (OMOP/PCORnet), enable FHIR/APIs and AI/ML-ready datasets, oversee ETL from Epic (Clarity/Caboodle/Cosmos), support faculty research and grant deliverables, manage staff, and operate within a federal recharge center framework.

Lead a centrally‑federated team of Data Stewards and Data Quality Analysts to operationalize data governance. Translate policies into stewardship processes, enforce consistent definitions and quality rules, oversee issue escalation and remediation, embed stewardship into projects, monitor KPIs, and partner with governance, engineering, and business stakeholders to maintain metadata, lineage, and data fitness.

Lead finance-focused data and analytics initiatives: build scalable data models and Power BI dashboards, develop a Finance AI roadmap, analyze margin/utilization drivers, support profitability and pricing strategies, and deliver executive-ready insights partnering with FP&A, Operations, IT and senior leadership.

Manage a team to produce repeatable reports and dashboards, ensure data integrity, support BI tool users, develop complex analytics, collaborate with Sales/Marketing/Customer Care, and design A/B and multivariate tests to drive measurable financial results.

Lead strategy and delivery of enterprise streaming and social data products that serve as authoritative sources of truth. Ensure AI-ready, governed, semantically consistent data with metadata, lineage, observability, SLAs, and adoption plans. Partner with analytics, engineering, and commercial teams to define metrics, manage external data sources, enforce data contracts, and drive AI and predictive use cases for music consumption and audience insights.

Lead a data platform and BI decision-support organization as a player-coach: set platform roadmap and standards, steer pod-based agile delivery, direct engineering work, design semantic/business-layer models, enable AI and automation (including prompt engineering), and partner with Commercial and SCOPs stakeholders to deliver adopted analytics products.

Lead development and implementation of systems converting farm production and geospatial data into greenhouse gas and sustainability metrics. Oversee data collection, stewardship, QA/QC, modeling (R, Roth C, Fieldprint-style tools), and reporting. Coordinate data pipelines with agronomy, engineering, and partners; ensure audit readiness, documentation, and traceability; support Scope 1–3 inventories, disclosures, and a centralized emissions data warehouse. Manage contractors, improve automation, and communicate findings to technical and leadership audiences.

Manage and maintain the donor database and advancement systems, process and reconcile gifts, generate reports and dashboards, analyze donor trends, oversee data integrity and security, supervise data staff, support fundraising campaigns, and ensure compliance with privacy and reporting requirements.

Lead acquisition, cleaning, validation, storage and protection of datasets to support reporting and analysis. Design and execute data architectures, policies and ETL pipelines (SSIS, ADF) using cloud platforms (Azure Synapse, Snowflake, Databricks). Deliver dimensional models, visualizations, and lifecycle governance to make complex data accessible and drive business insights.

Lead data-focused consulting projects end-to-end: manage stakeholders and teams, translate business problems into data solutions, perform hands-on SQL (and occasional Python) development, design data models and warehouses, build BI visualizations, run workshops, mitigate risks, and contribute to practice growth and mentorship.

The role involves managing data analytics and forecasting, driving revenue-focused insights, designing experiments, and building scalable models while leading a team of analysts.

As a Senior Data Steward Manager, you'll oversee data management and governance, ensure data quality, and work with cross-functional teams to meet data standards.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Lead design, development, and implementation of enterprise data pipelines and architectures. Optimize ETL/ELT processes, ensure data quality and governance, and build cloud-scale data solutions using Azure and Snowflake. Implement and maintain CI/CD pipelines, automate tasks with Python/PowerShell and Azure serverless, deploy and monitor GPT models in Azure OpenAI, and supervise and coach a data engineering team to support analytics and ML initiatives.

Lead global BI strategy, governance, platform modernization, and adoption across corporate and manufacturing functions. Own Microsoft Fabric and Power BI ecosystems, enable self-service analytics and semantic models, drive AI-enabled BI (Copilot), manage teams, and influence executive stakeholders to deliver governed, scalable analytics solutions and measurable business value.

Lead regional execution of master data governance and quality programs. Define standards, resolve complex data issues, drive remediation and integrations, support MDM applications, mentor stewards, and enable enterprise reporting and transformation initiatives.

Evaluate data management, reporting, and analytics controls for a large financial services client. Review data transformations, Python code, SQL queries, DBMS and Alteryx workflows, assess data lineage and access controls, validate remediation activities, test control effectiveness, and document findings and recommendations.

Serve as a marketing data steward: define metadata, monitor and enforce data quality, validate campaign tracking, support audience logic and testing, and collaborate with marketing pods, data engineering, and analytics to enable measurable, reliable campaign insights.

Manage and lead the Data Platform Operations team to ensure reliability, security, performance, and modernization of enterprise databases and cloud data platforms (SQL Server, Oracle, Snowflake, Azure services). Oversee production support, access governance, release/change management, monitoring, backup/recovery, performance tuning, cost/capacity management, vulnerability remediation, automation, runbooks, vendor relationships, and cross-team coordination.

Manage and improve corporate giving data systems: build and maintain Tableau/Excel dashboards, respond to ad hoc data requests, clean and govern community data, administer Bonterra/CyberGrants, and lead project management for giving programs and strategic initiatives while partnering with enterprise stakeholders.

Lead Compliance Data Analytics to research and analyze risk patterns (fraud, account takeover, payment fraud), deploy and iterate rules/models, partner with stakeholders to balance user experience and risk mitigation, drive business-centric analytics and strategic metrics, and build and manage a high-performing compliance analytics team.

Lead enterprise data strategy and analytics initiatives; build dashboards and reports (Power BI, Tableau); perform advanced analytics, predictive modeling and ETL (Python, SQL, Snowflake, Alteryx); advise leadership, deliver client reporting, mentor analysts, and develop self-service tools to scale insights.

Lead development and maintenance of equity analytic datasets, pipelines, and factor models to support quantitative research, portfolio risk and return attribution. Ensure data quality and process stability, deliver recurring and ad-hoc analytics, build dashboards, troubleshoot production issues, and provide technical and strategic guidance while mentoring junior staff and coordinating across business, technology, and vendor teams.