Top Remote Data & Analytics Jobs in Pittsburgh, PA

As a Data Engineer II, you will build and optimize data pipelines in Databricks, ensure high-quality data delivery, and support generative AI applications. You will collaborate with data scientists and AI engineers to provide reliable data infrastructures and meet business needs.

The Business Analyst will manage the Quote-to-Cash lifecycle, liaising between IT and functional teams, and ensuring successful implementation of system updates while documenting requirements.

Support sales intelligence by collecting, preparing, and analyzing sales and marketing data; maintain dashboards and data quality; run pipeline and win/loss analyses; translate findings into clear visualizations and reports for stakeholders.

Lead analytics work translating complex healthcare data into decision-ready insights. Own KPI definitions, Snowflake data marts with dbt, executive dashboards in QuickSight, and cross-functional analyses to inform strategy, product, and operations.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

The AV Safety Analytics Engineer will develop data analytics infrastructure for automated vehicle safety, utilizing cloud processing and statistical methods. Responsibilities include creating data visualizations, monitoring metrics, and ensuring data integrity across systems.

The Revenue Strategy & Planning Manager will develop actionable growth plans by analyzing data, building reports, and collaborating with Sales leaders to enhance B2B revenue strategies.

The RevOps Manager is responsible for optimizing sales execution through data analysis, CRM management, enablement initiatives, and strategic support for growth goals.

Lead fraud detection efforts by analyzing data, developing detection methods, collaborating with teams, optimizing fraud rules, and monitoring performance metrics.