Top Remote Data & Analytics Jobs in Pittsburgh, PA

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

Own enterprise analytics audits, implementations, data quality, attribution, segmentation, SQL analysis, BI dashboards, experimentation measurement, and client reporting. Serve as the senior analytics partner for major accounts, present insights to executives, train client and internal teams, and help define analytics and experimentation services. The role requires hands-on Adobe expertise, autonomous prioritization, technical collaboration, and the ability to connect organic and AI search performance to pipeline and revenue.

The Senior Clinical Trials IT Business Analyst bridges Clinical Development and IT, optimizing clinical trial technologies and managing system implementations, validations, and stakeholder engagement.

Conduct AI-powered web research, scraping, market analysis, and lead generation to identify clients, partners, sponsors, investors, and collaborators. Build and maintain intelligence databases, automate research workflows, verify and enrich data, analyze unstructured information, prioritize opportunities, and present actionable recommendations to leadership.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Own end-to-end analytics across marketing, revenue, product, and partnerships. Build reliable data models and semantic layers in a lakehouse, maintain dashboards and monitoring, investigate anomalies, conduct experimentation, and translate complex analyses into recommendations for senior stakeholders. The role requires advanced SQL, Python, data modeling, stakeholder communication, autonomy, and mentoring, with responsibility for improving data quality and building analytical standards in a high-growth startup.

Monitor news, media, social platforms, and events; prepare data for media analysis and reports; create Excel pivot tables, charts, and visualizations; write analytical insights; support research projects; and validate proprietary reputation-risk and stakeholder-view scoring. The intern will help deliver timely client alerts, briefings, and longer-term reports while collaborating with analysts and interpreting trends across business, culture, and politics.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.