Top Data & Analytics Jobs in Pittsburgh, PA

The QA Software Analyst ensures product quality through testing, validation, and collaboration with teams, while maintaining documentation and meeting schedules.

Lead enterprise BI capabilities: define BI roadmap, standardize KPIs, deliver dashboards and self-service analytics (Power BI/Tableau), oversee report lifecycle, governance, and data quality. Build and manage a BI team, partner with data engineering and business leaders, introduce AI-assisted analytics, and support audit readiness.

The Data Analyst will support cloud data warehouse migration, analyze data, write SQL queries, validate data quality, and collaborate with cross-functional teams to deliver scalable data solutions.

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.

The Fraud Data Analyst will develop dashboards, conduct data analysis, automate workflows, and collaborate with teams to improve fraud detection and business performance.

The Business Systems Analyst will support the finance team by defining and documenting business and functional requirements, analyzing systems, troubleshooting, and managing relationships with vendors and IT resources.

As an Automation Engineer, you will design and build automation solutions, optimize processes, and collaborate with teams across the organization to develop streamlined, data-driven systems. Responsibilities include delivering workflows, performing testing, providing support, and identifying continuous improvement opportunities.

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.