Top Data & Analytics Jobs

Lead data analysis, reporting, and storytelling for Health Insurance clients. Design studies and surveys, ensure questionnaire quality, analyze results, collaborate with client teams, and produce clear, actionable deliverables on schedule.

Build, operate, and improve large-scale data ingestion pipelines supporting autonomous driving AI systems. Responsibilities include debugging pipeline failures, handling corrupt and inconsistent data, optimizing Spark jobs, managing orchestration and retries, improving throughput and reliability, reducing operational toil, and supporting downstream annotation, data science, training, and evaluation teams.

Manage program data across MDA systems by establishing data governance, developing Integrated Data Management and Data Handling Plans, supporting data lifecycle (collection, validation, storage, dissemination, archival), ensuring integration and interoperability, defining test data requirements, supporting mission execution and data readiness for BI/AI/ML, performing data analysis to identify gaps, and maintaining metadata and data quality standards.

Analyze, clean, transform, and validate structured and unstructured data; maintain data pipelines; develop Power BI or similar dashboards; conduct exploratory analysis; automate reporting workflows; document data processes; and support federal data quality, governance, and AI-enabled analytics initiatives.

Ten-week summer internship focused on analytics and business transformation. Interns will analyze data, create insights and recommendations, support process improvement, explore AI-enabled tools, and contribute to data-driven decision-making. The role includes cross-functional collaboration, professional development, and a capstone project. Interns must work full-time in a hybrid setting and relocate to Canonsburg, Pennsylvania, or Houston, Texas.

Manages fundraising prospect portfolios through pipeline development, portfolio optimization, prospect assignment, and data integrity. Partners with gift officers to prioritize prospects, provide data-informed recommendations, and improve fundraising outcomes. Uses CRM systems, dashboards, reporting tools, business intelligence, and emerging AI technologies to generate actionable insights and improve operational efficiency. Collaborates across analytics, IT, and development teams while handling confidential information and managing multiple priorities.

Designs and maintains enterprise dimensional data models, semantic layers, metadata structures, and analytics architecture. The role applies star schema standards, validates model quality, supports governance and lineage, and aligns source, transformed, and published data layers. It partners with data engineers, business stakeholders, governance teams, and analytics users to improve scalability, performance, consistency, discoverability, and trusted reporting across the enterprise.

Lead client-facing architecture for Microsoft Data & AI solutions, translating business priorities into secure, governed, scalable architectures and modernization roadmaps. Design lakehouse, batch and streaming platforms using Fabric, Databricks, Azure, Power BI, and related governance tools. Architect AI-ready data platforms supporting generative AI, agentic AI, RAG, vector search, and enterprise data agents. Support presales, proposals, estimation, executive presentations, implementation guidance, and mentorship of architects and engineering teams.

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.

Own and architect marketing and storefront analytics systems, including metric definitions, single sources of truth, multi-touch attribution, marketing mix modeling foundations, semantic models, and data health metrics. Partner with marketing, storefront, and leadership teams to improve acquisition measurement, budget allocation, data quality, and decision-making across international markets.