Top Remote Data Engineer Jobs

As a Data Engineer, you will build and scale data pipelines, optimize advertising effectiveness, and handle large datasets using AWS, Airflow, and Snowflake.

Design and build enterprise-scale Snowflake environments, develop and optimize data pipelines with Python, dbt, and Airflow, lead client technical strategy, implement Snowflake security/compliance, and mentor junior engineers while collaborating with Snowflake, AWS, and Anthropic partners.

Design, build, and optimize cloud-native Databricks Lakehouse data pipelines on AWS. Implement governance (Unity Catalog, lineage, retention), medallion architecture, data quality frameworks, and performance tuning. Enable secure analytics, semantic layers, and self-service reporting. Collaborate with architects, cloud and cybersecurity teams, and produce documentation, operational procedures, and platform modernization deliverables.

Build and maintain data marts, materialized views, and pipelines in an AWS Aurora PostgreSQL environment. Integrate data with downstream BI platforms (MicroStrategy), design/optimize workflows for analytics and reporting, monitor pipeline health and data quality, and support federal data security compliance (FISMA/NIST 800-53). Collaborate with engineering and product teams to enable data-driven features.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Lead large-scale migration of document-heavy legacy systems into cloud, convert unstructured documents to analytics-ready data, build MuleSoft integrations and APIs, apply DataWeave transformations, ensure data quality, lineage, governance, and support Salesforce Data 360 implementation and cutover in Agile teams.

Design, build, and maintain ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

The role involves designing and optimizing data pipelines in Snowflake, developing ETL workflows, collaborating on data integration, ensuring data security, and performance monitoring.

The role involves building and maintaining financial data pipelines in Databricks, bridging Snowflake, and ensuring data accuracy for finance reporting.

The Data Engineer will design, build, and maintain data pipelines and cloud solutions, ensuring data quality and supporting analytics. Key tasks involve developing ETL processes, managing CI/CD, and collaborating with teams for data integration.

As a Data Engineer, you will build and maintain data ingestion pipelines, develop data transformations, and migrate SQL Server processes to Azure-native solutions, supporting marketing campaigns and analytics teams.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

Design, build, and maintain scalable batch and streaming data pipelines, storage, and integrations on AWS. Own features end-to-end, write testable code, collaborate with cross-functional teams, support data scientists with semantic models, ensure data quality, and occasionally travel to customer sites for deployment and integration.

Design, build, and maintain Databricks-based data pipelines and ETL/ELT processes using Python and SQL. Optimize workflows for performance and cost, support data modeling and cloud integrations (Azure/AWS/GCP), monitor and troubleshoot pipelines, document best practices, and collaborate with cross-functional teams.

Build and maintain scalable, reliable data pipelines and ingestion processes; support data integration, quality, governance, and cloud-based analytics platforms; troubleshoot partner/customer data environments; document standards and reference architectures; automate processes and ensure security and compliance for enterprise data solutions.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

Develop custom connectors on Airbyte for data ingestion from various platforms, ensuring accurate data integration and transformation. Collaborate with stakeholders and perform testing for reliability.

The Data Engineer / Data Project Lead manages data projects, designs pipelines, leads a technical team, ensures data quality, and supports decision-making for Head Start programs.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.