Top Senior Analysis Reporting Jobs in Pittsburgh, PA

Defines and governs Asset Management Group data architecture, including target-state platforms, lakehouse modernization, Hadoop migration, data governance, integration, and warehouse strategy. Partners with engineering, enterprise architecture, application development, and business teams to deliver scalable, secure, high-quality data solutions. Leads architecture reviews, technology evaluations, modernization roadmaps, and strategic initiatives supporting analytics, AI/ML, digital applications, and operational intelligence. Mentors technical teams and provides guidance throughout solution delivery.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Owns end-to-end delivery of business intelligence reporting and analytics solutions. Responsibilities include requirements gathering, business process analysis, KPI development, dashboard and report creation, data quality validation, UAT planning and coordination, stakeholder liaison, reporting lifecycle management, and continuous improvement. The role uses Power BI and Microsoft tools to support executive decision-making, optimize reporting processes, improve data integrity, and promote adoption of analytics capabilities across the organization.

Design and architect scalable healthcare data systems, core data models, and production data pipelines. Integrate and normalize healthcare data using TUVA, OMOP, and FHIR standards; establish data quality and governance; optimize performance; deploy secure cloud infrastructure; ensure regulatory compliance; and build bi-directional EMR integrations. The role also requires independent technical decision-making, test automation, CI/CD, and DevOps practices in a fast-paced healthcare technology startup.

Leads the design, development, migration, and deployment of scalable data pipelines and cloud data solutions. Establishes reusable ingestion, transformation, quality, governance, compliance, and orchestration processes. Partners with business and BI stakeholders to define data requirements, support financial and operational reporting, and enable AI use cases. Requires expertise in cloud and big data technologies, DevOps, CI/CD, data management, and governance, with preferred experience in Databricks, Snowflake, data warehousing, and finance-domain data.

Designs data models, ETL pipelines, semantic models, reports, and dashboards supporting business objectives. Conducts requirements sessions and data analysis, defines source-to-target mappings and transformations, and consolidates data into platforms such as data lakes and warehouses. Collaborates with analytics teams, clients, and regional stakeholders to deliver scalable business intelligence solutions. The role also involves technical assessments, cloud data platforms, DevOps practices, and modern data governance tools.

Designs and scales Snowflake-based healthcare data and AI solutions, including Cortex AI agents, LLM functions, semantic search, RAG pipelines, Streamlit applications, and Snowpark data workflows. Builds ELT pipelines, optimizes Snowflake performance, implements Data Vault or Medallion architectures, and manages governance, security, HIPAA compliance, lineage, and access controls. Partners with technical and clinical stakeholders while serving as a Snowflake subject matter expert.

Build and migrate production data engineering and machine learning workloads to Databricks on Azure. Assess on-premises SQL Server and SSIS systems, develop PySpark and Spark SQL pipelines, implement governance with Unity Catalog, and create reusable CI/CD assets. Work directly with Department of Veterans Affairs teams through requirements sessions, workshops, training, and documentation. Depending on focus, build ML workflows or modernize ETL, reporting, semantic models, and Power BI solutions.

Leads the design, development, testing, tuning, and optimization of financial crimes detection models across AML, fraud, KYC, and sanctions. Performs statistical analysis, threshold calibration, back-testing, model efficacy assessments, feature engineering, and root cause analysis. Configures financial crimes platforms, monitors model performance, establishes testing and governance standards, and partners with Model Risk Management and leadership on validation, audit, regulatory, and risk-related initiatives.

Own executive reporting and data strategy for autonomous vehicle system performance. Lead the weekly Software Performance Review, develop board-level reporting, analyze robotics and autonomy metrics, translate technical findings into strategic recommendations, and establish data visualization standards. Partner cross-functionally across engineering and performance teams, create data-driven feedback loops, and mentor Business Analytics and Business Intelligence engineers.