Top Remote Data Engineer Jobs in Raleigh, NC

Analyze large and diverse datasets to uncover insights, develop/train/deploy machine learning models, design data preprocessing pipelines, and create visualizations and reports. Collaborate with cross-functional teams to build predictive models, recommendation systems, and optimization algorithms to drive business improvements.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

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.

Design, build, and maintain scalable ETL/ELT pipelines ingesting travel-related data from multiple sources. Integrate and model data in cloud warehouses (Snowflake/BigQuery/Redshift), implement data quality and monitoring, support real-time streaming, and ensure data governance and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, and optimization.

Design, develop, test, maintain, and support secure, scalable data solutions using Snowflake and Databricks. Lead technical design, author specifications, review code, mentor engineers, ensure SDLC, audit/security compliance, and drive technical strategy while collaborating with architects and business partners. Participate in Agile ceremonies, be on-call as needed, and travel occasionally.

Build and maintain scalable cloud data pipelines and governance to support analytics across claims, EHR, and real-time patient data. Deploy and operate Snowflake/dbt/AWS-based infrastructure, enable real-time analytics, partner with clinical, product, and engineering teams, and deliver insights that inform care delivery, payer collaboration, and business decisions.

Lead architecture and technical strategy for enterprise-scale cloud data platforms. Design scalable ETL/ELT pipelines and data models, drive governance, implement AI-assisted engineering tools, mentor senior engineers, and ensure secure, performant, and maintainable data solutions.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

The Data Engineer at Anteriad will design, build, and optimize data pipelines, manage data integration processes, support analytics efforts, and collaborate with stakeholders to deliver scalable data solutions.

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.

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 and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

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

Design, build, monitor, and troubleshoot ETL pipelines (Azure Data Factory) to move and transform large datasets from sources like Salesforce and SFTP into Snowflake. Implement SQL-based data cleansing and quality checks, document data lineage and mapping, collaborate with QA/UAT, and recommend new integration tools and efficiencies.

Build and maintain data pipelines, applications, workflows, and governance in Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot/MRO capacity, throughput, and bottlenecks, integrate ERP/MRO data into simulation models, deliver decision-support briefings, and support platform monitoring, automations, and client engagement.

Support a Department of State implementation by assessing data policies and architecture, developing data pipelines, cleaning and integrating datasets (using Palantir Foundry), and producing analyses and visualizations. Create governance documentation, catalog data fidelity issues, coordinate with non-technical users, and recommend improvements to meet analytical and mission goals.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Design, build, and maintain cloud-native data pipelines (Ingest→Transform→Expose) using Airflow, Spark, dbt, and Iceberg. Ingest and normalize HL7/FHIR/OMOP and other structured/unstructured data for analytics and AI, implement schema versioning and governance, ensure interoperability and compliance with DoD Responsible AI and NIST frameworks, and collaborate with DevSecOps and Data Science teams to deliver production-ready datasets.