Top Senior Analysis Reporting Jobs in Pittsburgh, PA

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Designs and governs enterprise data architecture for on-premises warehouses, Hadoop, lakehouse, and graph environments. Leads data movement controls, reconciliation, orchestration, catalog governance, query federation, lineage, security, and auditability. Partners with business and technology leaders on requirements, roadmaps, design approvals, proofs of concept, compliance, and modernization. Requires expertise in data modeling, ETL/ELT, distributed systems, Hadoop, data warehousing, and enterprise architecture.

Manage digital analytics supporting eCommerce and Growth Marketing strategy. Translate complex business questions into actionable insights, dashboards, measurement plans, and recommendations. Analyze customer behavior, conversion, revenue, marketing effectiveness, and site performance while ensuring accurate tagging, event tracking, data quality, and analytics implementation. Partner cross-functionally on A/B testing, personalization, digital initiatives, governance, and leadership reporting to identify growth opportunities and improve decision-making.

Leads data reporting and analytics initiatives by partnering with stakeholders, identifying business information needs, extracting and analyzing data, developing solutions, and presenting insights through clear reports, dashboards, and other communications. Provides guidance and training to less-experienced teammates, manages complex assignments, and monitors project accuracy and progress. Requires a bachelor's degree in a quantitative or information technology discipline and 7–10 years of data analytics or business intelligence experience.

Designs and maintains operational data infrastructure for smart manufacturing, including pipelines, data warehouses, ETL/ELT processes, time-series data systems, integrations, governance, and trusted datasets. Uses SQL and Python to support reporting, analytics, automation, dashboards, and operational decision-making. Partners across manufacturing and enterprise systems, investigates complex data challenges, evaluates emerging technologies, and supports advanced analytics and digital transformation initiatives.

Manage macroeconomic and financial datasets for economic forecasting, regulatory stress testing, and reserve estimation. Responsibilities include data collection, updates, validation, revision review, documentation, model-development support, vendor collaboration, and responding to ad hoc requests. The role requires coordinating with modelers and bank teams while delivering accurate results on schedule. Strong quantitative, communication, organizational, and analytical skills are essential; R or Python programming is preferred.

Develop reporting, analytics, metrics, and dashboards to optimize autonomous-vehicle data collection and software development processes. Partner with data scientists, software leaders, and cross-functional stakeholders to prioritize data sourcing, build scalable data pipelines, monitor operational processes, and communicate insights. The role uses on-road and simulation data to support Aurora Driver development and commercialization.

The Data Engineer will develop and document research data products, contribute to scientific papers and findings, lead research efforts, coordinate with academic partners, and propose independent research goals. The role also includes project management, technical documentation, meetings, and collaboration with statistical developers, data engineers, and researchers. Experience with Apache Airflow, SQL, data visualization, web development, statistics, analytics, and agile software development is desired.

Leads the Analytics Engineering function, owning Databricks semantic layers, Unity Catalog metric views, Gold-layer data marts, Silver-to-Gold promotion logic, and reusable data products. Establishes governance, documentation, architecture, and quality standards while partnering with Data Engineering, BI, Governance, Informatics, and QA. Builds and develops the analytics engineering team, ensuring consistent business metrics across dashboards, partner feeds, internal analytics, and AI model inputs.

Leads Northwest Bank’s enterprise data governance program, including data policies, quality rules, lifecycle management, metadata, master data, stewardship, governance tooling, and remediation. Facilitates Data Working Groups, investigates root causes, manages governance metrics and reporting, demonstrates tools, documents processes, and supports training. Partners across a matrixed organization to ensure data assets meet standards, controls, regulatory requirements, and business needs.