Top Remote Data Engineer Jobs in Portland, OR

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Staff Data Engineer leading architecture and technical direction for real-time and batch motorsports data platforms. Responsibilities include telemetry ingestion, streaming systems, data quality, observability, cloud platforms, reliability, incident response, technical standards, roadmap planning, stakeholder influence, and mentoring. The role delivers scalable solutions supporting race-day operations and cross-functional analytical products.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Designs, builds, and maintains Snowflake-based enterprise data warehouse solutions, including data architecture, ETL/ELT pipelines, security, deployment, data quality, modeling, and performance optimization. Partners with integration, analytics, governance, business intelligence, and architecture teams. Supports production operations, resolves ingestion and transformation issues, develops AWS-to-Snowflake scripts, documents implementations, and translates business requirements into scalable data solutions.

Designs, develops, and maintains scalable ETL/ELT pipelines and data platform capabilities for high-volume healthcare data. Responsibilities include complex SQL development, Airflow orchestration, dbt transformations, data quality frameworks, pipeline monitoring, troubleshooting, performance optimization, and documentation. The role collaborates with Data Operations, Product, Analytics, Data Science, and clinical teams to deliver reliable data for analytics, reporting, product features, and AI/ML initiatives.

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.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs, develops, maintains, and improves software applications and business processes. Responsibilities include full lifecycle development, troubleshooting, performance and reliability improvements, CI/CD collaboration, requirements analysis, technology research, package maintenance, portfolio planning, and mentoring junior engineers. The role requires independent work, cross-functional collaboration, and strong software development, documentation, and problem-solving skills.

Build and maintain scalable Python-based data pipelines and cloud-native solutions supporting U.S. Army financial-system modernization. Develop ETL/ELT workflows using AWS Glue, Lambda, S3, and CloudWatch; integrate APIs and diverse file formats; improve data reliability, performance, security, and governance; implement testing and monitoring; and collaborate with engineers and stakeholders in an Agile environment.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Develop and maintain data pipelines, backend programs, and integrations in Azure Government Cloud. Transform and cleanse data, evolve scripts and notebooks, manage relational databases, support data quality, document technical processes, and collaborate with engineers, geospatial experts, scientists, and government clients. The role also involves project coordination, status reporting, client travel, and compliance with government background and security requirements.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Designs, develops, and maintains scalable data pipelines, data warehouses, databases, and analytical systems. Responsibilities include ETL implementation, data modeling, performance monitoring, troubleshooting, infrastructure optimization, process automation, and collaboration with cross-functional teams to support reporting and analytics. Requires 5+ years of data engineering experience and a relevant bachelor’s degree. Financial services experience is preferred.

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective 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.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Build and operate batch and streaming data pipelines on Google Cloud for analytics and machine learning. Ingest, transform, validate, and curate datasets in BigQuery; implement data quality, governance, security, monitoring, Terraform infrastructure, and CI/CD. Provide technical direction, lead code reviews, and document data processes. The role requires cloud data engineering experience, technical team leadership, strong SQL and Python skills, and eligibility for a federal Public Trust investigation.

Develop and maintain data pipelines, infrastructure, and integrations supporting analytics, data science, and business intelligence. Ensure data quality and integrity, optimize workflows, and collaborate with Operations, IT, Accounting, HR, Property Services, Marketing, and other departments. The role requires SQL Server, programming, shell scripting, cloud, containerization, version control, and data pipeline knowledge, with occasional travel and flexible scheduling as project deadlines require.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Build and maintain secure data pipelines and integrations across business platforms, laboratory systems, analytical instruments, and messy data sources. Own schema design, orchestration, monitoring, quality controls, documentation, and compliant data isolation for export-controlled information. Create a unified lakehouse foundation supporting analytics, dashboards, machine learning, R&D, manufacturing, and operations. Partner with scientists and technical teams to integrate HPLC, GC, Invert Bio, APIs, spreadsheets, PDFs, and semi-structured records.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.