Top Remote Data Engineer Jobs in Philadelphia, PA

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

Design, build, and maintain scalable ELT data pipelines and models to power reporting, analytics, and operational workflows. Troubleshoot data quality and performance, participate in incident response, support CI/CD deployments, collaborate with cross-functional teams, and contribute to data platform modernization and engineering best practices.

The AI Data Engineer will build data pipelines and semantic models, implementing Snowflake and dbt for AI products, focusing on data accuracy and usability for LLM tools.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

The Data Engineer will build and maintain scalable data pipelines, ensure data quality, and collaborate with teams to meet data requirements for federal government projects.

As a Senior Data Engineer, you will develop data pipelines, architect innovative data products, and integrate AI/ML models to enhance business insights.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Hands-on Senior Data Engineer to maintain and optimize an Oracle-centric enterprise data warehouse, manage cloud-to-Oracle integrations, evolve ETL/ELT pipelines, and help build a hybrid lakehouse (Apache Iceberg, OCI Object Storage, Parquet). Requires performance tuning, Linux administration, Java debugging, and close collaboration with senior client stakeholders. Contractor role with possible conversion to full-time.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Lead and define architectural solutions for data pipelines, mentor teams, optimize ETL/ELT processes, and ensure data governance in a client-facing environment.

The Principal Data Platform Engineer leads the architecture and development of the enterprise data platform, focusing on analytics, data science, and AI/ML capabilities. They establish standards for data quality and observability, design data ingestion and processing patterns, and mentor team members to enhance technical capabilities.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Design, build, and maintain scalable Azure data pipelines and platforms using Azure Databricks, Synapse, Fabric, and Data Factory. Implement IaC (Bicep/ARM/Terraform), CI/CD with Azure DevOps, optimize Spark jobs, monitor platform stability, and collaborate with architects, BI developers, and clients to meet Azure best practices and business requirements.

Lead technical strategy and roadmap for an enterprise Databricks-based data platform. Drive governance, metadata, lineage, and semantic layer standards; build reusable data products; enable self-service analytics and scalable ML operations; mentor engineers and align platform investments with business outcomes.

Lead design and delivery of a cloud-based data platform: implement CDC/streaming ingestion, modeling, transformations, cross-region DB replication, observability, governance (PII, lineage, access), and enable downstream marts while mentoring engineers and driving IaC/automation-first practices.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.