Top Remote Data & Analytics Jobs in Pittsburgh, PA

The role involves optimizing and managing Salesforce operations for various departments, driving Quote-to-Cash processes, and ensuring compliance while collaborating with other teams.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Data Engineer will build real-time data pipelines for pricing algorithms, collaborate with teams, and contribute to batch data workflows.

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 Senior Portfolio Lead will oversee data management services for clients, ensuring quality deliverables, strategic oversight, and optimal clinical data flows, while leading cross-functional teams and managing complex client relationships.

Lead digital measurement strategy for web and digital marketing. Build and maintain dashboards, integrate multi-platform data, perform tagging QA, run A/B test analyses, and deliver insights to optimize campaign effectiveness, UX, and ROAS.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

The role involves assessing fraud program effectiveness, compliance with regulations, and providing strategic insights for risk management within the financial services industry.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

Design and build data warehouse architecture, develop ETL/ELT pipelines, implement data governance, and partner with teams to improve data systems.