Top Remote Senior Level Data Engineering Jobs in Philadelphia, PA

Responsible for architecting the technical vision for bioinformatics systems, ensuring compliance, automating workflows, and integrating data analysis with clinical operations.

Lead the design, development, and maintenance of large-scale data engineering solutions using Azure and Microsoft Fabric, working with stakeholders and mentoring junior staff.

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.

This role involves defining data platform standards and integration patterns, mentoring engineers, and maintaining best practices in Azure and Databricks environments. The lead will ensure efficient data management and governance across global teams while advocating for DataOps and CI/CD practices.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

The Lead Analytics Engineer will model revenue, specifically subscription revenue, manage financial KPIs, and ensure reconciliation with source systems like Stripe, while adhering to data governance standards.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

The Lead Data Consultant will design and deliver scalable data solutions, manage project teams, and optimize client data environments, ensuring effective project execution and communication.

The Data Architect will modernize the data ecosystem, design data pipelines, ensure data governance, and align data strategies with business needs to enhance analytics capabilities.

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.