Top Remote Data & Analytics Jobs in Pittsburgh, PA

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

The Solution Engineer - Data Engineering Specialist will provide technical leadership in the financial services sector, collaborating with sales teams to demonstrate Snowflake's data platform capabilities while engaging with technical and executive stakeholders.

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Develop and run SAS programs to validate and clean surveillance and research data, create codebooks and data plans, perform geospatial and molecular HIV analyses in R, collaborate on novel code and reporting, and support dissemination to federal and external partners while ensuring data quality and documentation.

Develop and validate surveillance, clinical, and research datasets using SAS and R. Identify and resolve data discrepancies, create codebooks and data management plans, perform geospatial and molecular HIV analyses, collaborate on code development, and support dissemination and reporting to federal and external partners.

Partner with clinical and operations leaders to build scalable real-time reporting, measure team performance and SLAs, perform root-cause analyses, advance data modeling and best practices, and enable stakeholder self-service using BI and AI capabilities.

Lead growth analytics for acquisition and activation funnels: measure channel performance, build data models and KPI reporting, run experiment analysis, and translate insights into business recommendations. Partner with Growth product team and leadership to visualize results and drive strategy.

Provide user support and analyst services for business systems with a QMS focus: gather and document requirements, configure and test solutions, support UAT, monitor data quality, develop reports/dashboards, and deliver training.

Lead HR Service & Digital Experience analytics by owning reporting, ensuring data integrity, and translating operational and contractual HR data into actionable insights for senior leaders to improve caregiver and leader experiences.

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

Design and build scalable data solutions and robust pipelines on Palantir Foundry to enable analytics and AI/ML. Model enterprise datasets, develop Foundry applications and custom frontends, integrate cloud data services (AWS, Snowflake), apply DevOps practices (CI/CD, containers), and optimize distributed systems for performance. Insurance domain experience is a plus.

Perform end-to-end data analysis and transformation for large-scale P&C insurance datasets. Develop source-to-target mappings, validate and profile data, optimize complex SQL queries, and collaborate with business SMEs and technical teams to ensure data quality, standardization, and accurate technical documentation.

Design and implement AWS-based data architectures and lakehouse solutions, lead data engineering teams, build ETL and real-time pipelines (Kafka/Kinesis), use DBT and Airbyte for ingestion and transformation, orchestrate workflows with Airflow/MWAA, enforce security/compliance, and guide infra automation with Terraform/CloudFormation and CI/CD.

Lead design and implementation of BI solutions, maintain Power BI relational models, build enterprise dashboards and self-service analytics, translate business requirements into technical specifications, lead cross-functional analytics projects, apply AI-enhanced analytics, and promote data governance and data literacy across Finance, HR, and conservation programs.

Lead enterprise analytics strategy and execution, build scalable self-service BI and dashboards, manage complex analytics projects, partner with cross-functional stakeholders, perform advanced analyses as subject-matter expert, and drive AI-enhanced analytics adoption to inform strategic decision-making.