Top Senior Data Management Jobs in Louisville, KY

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

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.

Build and lead the Compliance data function, owning data products for Customer Risk, Transaction Monitoring, AML, Sanctions. Design and implement data risk frameworks (quality, usage, lineage), enforce data contracts between producers and consumers, maintain end-to-end lineage with Engineering, and drive a risk-based data roadmap aligned with compliance requirements.

As a Manager in Data Governance at PwC, you will lead data governance strategies, supervise teams, manage client accounts, analyze data for decision-making, and ensure compliance with standards.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Lead a team of data and infrastructure engineers to develop cloud-based data systems and solutions for analytics, ensuring best practices in data management and MLOps.

Support data-driven operations by sourcing, integrating, validating, and transforming data; build and maintain dashboards, agency scorecards, and reports; analyze KPIs to identify improvement and cross-sell opportunities; measure campaign effectiveness; ensure data quality; partner with stakeholders and technology teams; support monthly reviews and ad-hoc analytics requests.

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.

The Manager R&D Data Steward oversees data governance, quality management, and stewardship of R&D data, ensuring compliance and integrity across data products and leading a team to support data management initiatives.

As a Senior Manager, R&D Data Steward, you will oversee stewardship of Pharma R&D data, ensuring data quality and compliance across the R&D value chain. You will lead a team, operationalize data governance, and drive improvements in data management processes, leveraging automation and cross-functional collaboration to enhance data-driven insights.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics processes.

The Manager leads business intelligence and data operations, establishes KPI governance, oversees executive reporting, and ensures data-driven decision-making across teams.

The Senior Manager will oversee operations across technology functions, lead strategic initiatives in Data & AI, and ensure effective management of technology projects and resources. Responsibilities include engaging stakeholders, identifying risks, and fostering continuous improvement within the team.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance 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 a global, hands-on data engineering team building large-scale Databricks/PySpark pipelines, production datasets, customer-facing data feeds, AI-ready analytical datasets, and data quality/observability systems. Partner cross-functionally, use AI coding tools to accelerate delivery, and ensure operational excellence, reliability, and documentation for investor-facing products.

Administer and maintain CRM and data systems (EveryAction, Salesforce, BigQuery, CARTO), respond to staff and affiliate data/tech requests, ensure data quality and de-duplication, create and deliver training materials, build network capacity, manage vendor/consultant relationships, and travel occasionally for in-person trainings and support.

Manage a team of analysts for PBM analytics, driving insights and scalable data products. Partner with business leaders for effective reporting and analysis.

The Senior Manager, Data Steward (AI) will lead data stewardship initiatives in Pharma R&D, ensuring data quality and governance while mentoring a team and collaborating across departments to enhance data usability for insights and compliance.

The Clinical Data Manager will manage research databases, oversee EHR to EDC automation, ensure data quality, and perform statistical analyses.

The Data Product Manager (DPM) leads the lifecycle and development of data-centric products, aligning them with business goals and collaborating with cross-functional teams to deliver value and insights from data products.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.