Top Remote Data & Analytics Jobs in Pittsburgh, PA

Investigate offline and direct mail channel performance, attribution, vendor data, and measurement methodologies. Build response, lift, allocation, and regression models; manage direct mail testing data pulls and mail merges; create KPI dashboards; and communicate actionable recommendations to marketing stakeholders. The role requires proactively identifying opportunities, validating data and vendor claims, and translating complex analytical findings into business decisions.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Define enterprise data modeling standards, architect pipelines and integrations, establish engineering best practices, troubleshoot complex platform issues, and guide modernization and cloud migration initiatives. Provide technical leadership and mentorship while ensuring data platforms meet security, quality, governance, reliability, performance, and maintainability standards.

Lead advanced product analytics for a consumer product team by analyzing complex data, designing experiments, applying statistical and causal inference methods, developing metrics and reporting tools, and delivering actionable recommendations to cross-functional stakeholders and senior leadership. The role requires strong Python or R and SQL skills, product analytics experience, AI-assisted analytics proficiency, and the ability to influence product strategy.

Develop and maintain automated data pipelines, API-driven workflows, and data integrations using Python, SQL, Databricks, and data lake architectures. Improve data quality through validation, schema design, and provider data mapping across diverse sources. Build analytics-ready datasets for predictive analysis, Power BI reporting, and GIS-based access analyses. Document data flows and technical configurations, support knowledge transfer, and collaborate with analysts, product owners, contractors, and government stakeholders.

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

Leads data analytics and operational transformation for the Magnetic Resonance team. Oversees exploratory analysis, predictive modeling, forecasting frameworks, metrics, visualizations, and scenario-based insights to improve delivery and installation performance. Partners with regional, segment, business, and technical stakeholders to support planning, strategic initiatives, data transparency, and continuous improvement. Travel to manufacturing or customer locations may be required.

Lead enterprise metadata strategy, standards, governance, capture, curation, validation, lineage, and lifecycle management across data products and business assets. Partner with data governance, engineering, quality, analytics, modeling, and stewardship teams to improve data trust, discoverability, reuse, and AI readiness. Drive metadata automation, catalog synchronization, certification support, issue remediation, and stakeholder communications across a complex enterprise data environment.

Owns the company’s BI platform and data strategy across clinical, operations, finance, and HR functions. Responsibilities include setting the analytics product roadmap, governing data definitions and PHI, managing platform architecture, building dbt models and dashboards, delivering healthcare quality and value-based-care analytics, deploying conversational AI, coordinating vendors and multi-team programs, and developing an analytics team. The role requires hands-on ownership of clinical data, EHR integrations, semantic modeling, governance, and executive reporting.

Build and maintain enterprise data pipelines and infrastructure supporting natural-language querying, semantic search, knowledge graphs, and GraphRAG for the SEC. Responsibilities include onboarding data sources, processing XBRL and financial filing data, managing metadata and lineage, validating data quality, monitoring pipeline health, resolving integration issues, and using AWS data services. The role also requires compliance with federal security standards and collaboration with the Data Architect.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.