Top Senior Data & Analytics Jobs in Pittsburgh, PA

The Senior Manager will lead data analytics and pricing strategies in CAD, improving data processes, analytical workflows, and decision-making capabilities while managing a high-performing analytics team and collaborating with various stakeholders.

Lead a team delivering sales operations analytics, building scalable reports and data models, standardizing KPIs, driving data governance, partnering cross-functionally to improve productivity, financial visibility, and decision-making using SQL and Power BI.

Develop and maintain Mainframe FOCUS programs and Cognos reports, perform data analysis, write specifications from existing programs, and support project management and stakeholder communication for a 12–16 month contract.

Design, implement, and operationalize an enterprise data governance framework. Lead governance councils, define stewardship and metadata practices, ensure regulatory compliance, integrate AI governance, monitor KPIs, and drive adoption across business, technology, risk, and compliance.

Design and develop with Markit EDM tool, working on OLTP and data warehousing environments, and integrate business requirements into architecture.

The Data Architect will design and implement scalable data architectures and ETL frameworks on GCP, collaborating with business stakeholders to deliver actionable insights and ensuring data governance and security.

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.

Lead development of large-scale data analysis and validation workflows for autonomous driving ML. Design metrics, build datasets, run statistical error and regression analyses, collaborate with ML and software teams, and maintain AWS/Kubernetes orchestration for large-scale evaluation.

Serve as a consultative analytics partner applying SQL and visualization skills to analyze pharmacy and medical claims, build dashboards, synthesize multi-source data, inform engineering teams, support data governance, and deliver actionable insights to internal and external stakeholders.

Design, build, and optimize data pipelines for analytics and ML. Collaborate with BI teams and improve data platform performance.