Top Senior Data & Analytics Jobs in Houston, TX

Lead the development and implementation of data architecture strategies, mentor a team to deliver solutions, and collaborate with stakeholders to enhance resources while ensuring project success and quality outcomes.

The Sr Manager of Payor Performance Analytics drives financial, analytical, and operational components of payer contracting, overseeing reimbursement modeling and ensuring successful contract negotiations and implementation.

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

The role involves leading data analytics to drive strategy, conduct risk assessments, and improve data systems within the data strategy team.

Lead and manage a large data management and quantitative analysis team to prepare datasets, perform statistical, financial, and econometric modeling, oversee data-related technology improvements, manage vendor relationships, ensure service quality per SLAs, and deliver client-facing documentation, reporting, and insights.

The SAS Programmer/Analyst will automate processes for recurring analyses, develop reports and dashboards, and build reusable technology components for statistical analysis.

The SAP Business Analyst will gather requirements, define RTR needs, lead workshops, and support SAP S/4HANA implementation with a focus on the CO module.

The Power BI Architect will lead BI platform design and governance, ensuring scalable solutions and guiding BI Engineers, while collaborating with Azure teams.

The Senior Data Architect will design and implement data architecture strategies, integrating Oracle EBS with cloud services, overseeing data governance, and collaborating with cross-functional teams to optimize data workflows.

The role requires a highly experienced Databricks Architect to lead data engineering efforts, focusing on PySpark and ETL/ELT pipeline development, while optimizing data models on cloud platforms such as Azure, AWS, or GCP.