Top Remote Data Engineering Jobs

The role involves architecting data solutions, building data products in Snowflake, establishing data standards, and enabling self-service data access while ensuring data quality and compliance.

Design and optimize end-to-end financial data pipelines and automated validations; build and maintain production-grade SQL repositories; perform forensic analytics to resolve discrepancies; serve as Finxact SME; lead governance, reporting automation, and cross-functional projects; mentor junior analysts and partner with Product and Engineering.

Lead legacy-to-SaaS data migrations: design and execute ETL, extract/transform/load data, validate integrity, map business workflows to target models, engage clients, document processes, support governance, and train stakeholders to ensure accurate, secure conversions.

The Database Administrator will manage Oracle Banner ERPs, supporting installations, upgrades, and configurations while ensuring database security and performance.

The Data Architect will design and build a unified reporting and analytics foundation for Taylor Corporation, transforming disparate data sources into a cohesive model to support decision-making. Responsibilities include collaborating across teams, leading data modeling efforts, and ensuring effective reporting practices across the division.

The Senior Data Engineer designs and maintains data pipelines, ensures data quality, and supports advanced analytics for U.S. Naval Aviation. They oversee data engineering practices and contribute to enterprise-scale architecture initiatives.

The Data Engineer will build and maintain data pipelines, ensuring high-quality and scalable data systems while working on analytics and machine learning accessibility.

Lead risk modeling initiatives from data exploration to deployment, defining methodologies, advising clients, and integrating models into platforms.

The Lead GTM Analytics Engineer designs and manages the data model for GTM execution, ensuring accurate metrics and insights across Sales, Marketing, Customer Success, and Partnerships to drive revenue performance.

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.