Top Senior Data Management Jobs in Sacramento, CA

Lead data-driven strategies and analytics at PwC, motivating teams to improve business performance and decision-making through advanced techniques and collaboration.

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 Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

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.

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

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

Lead the enterprise data platform strategy and architecture, driving Snowflake/dbt-based platform evolution, self-service Data Mesh and medallion models. Build AI-ready pipelines, RAG systems, and observability/cost frameworks while managing a central data team, supporting federated BI, and executing hands-on technical work.

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.

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

The Senior Manager of Data Engineering will build a data platform, lead a team, and ensure data accessibility to support AI and business operations.

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.

The Senior Manager, Performance Data Operations leads the Population Health Impact team, driving performance improvement initiatives, building complex datasets, and communicating insights to stakeholders. Responsibilities include data analytics, program management, and fostering cross-functional relationships.

Lead end-to-end data migration strategy and execution for complex franchise partners—perform discovery, map legacy data, troubleshoot imports, align stakeholders, and build scalable playbooks to standardize data delivery and ensure seamless platform transitions.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

Lead a data services team to design, automate, and oversee data processes. Oversee data extraction, transformation, modeling, validation, and quality reviews. Mentor and manage staff, monitor metrics, manage workloads, collaborate with stakeholders, ensure deliverable accuracy and confidentiality, and drive continuous improvement and strategic 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.

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

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

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 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 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.

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.