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Work From Home Data Engineer Jobs in Oregon (NOW HIRING)

Tebra

Data Engineer

OR · Remote

$114K - $137K/yr

You'll work on modern data platforms and gain experience building solutions that support both model ... Participate in engineering design discussions and contribute to technical decisions around data ...

Fieldwire

Data Engineer

OR · Remote

$120K - $150K/yr

You will work closely with Data Insights Managers, Finance, Revenue Operations, and Product to ... Design, build, and maintain automated data pipelines that move data from source systems (Salesforce ...

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Work From Home Data Engineer information

What are the key skills and qualifications needed to thrive as a work from home data engineer, and why are they important?

To thrive as a Work From Home Data Engineer, you need strong programming skills (typically in Python, SQL, or Scala), a solid understanding of data modeling, and a degree in computer science or a related field. Familiarity with data engineering tools like Apache Spark, Hadoop, ETL frameworks, and cloud platforms such as AWS or Azure, along with relevant certifications, is highly valued. Excellent problem-solving abilities, self-motivation, and effective remote communication are essential soft skills for remote collaboration. These skills are crucial for building reliable data pipelines, ensuring data quality, and contributing effectively to distributed teams.

How do remote data engineers typically collaborate with team members and stakeholders?

Remote Data Engineers usually rely on a combination of communication and project management tools, such as Slack, Zoom, Jira, and GitHub, to stay connected with their teams. Regular virtual meetings, code reviews, and shared documentation help ensure alignment on project goals and data solutions. Collaboration often involves working closely with data analysts, software engineers, and product managers to understand requirements, design robust data pipelines, and troubleshoot issues. Proactive communication and strong documentation skills are essential for success in a distributed environment.

What does a work from home data engineer do?

A Work From Home Data Engineer is responsible for designing, constructing, and maintaining systems and architecture that allow organizations to collect, store, and analyze large amounts of data. Working remotely, they build and manage data pipelines, ensure data quality, and collaborate with other teams to support data-driven decision-making. These engineers often use programming languages like Python or SQL and work with cloud platforms to process and manage data securely from their home offices.

What is the difference between Work From Home Data Engineer vs Data Analyst?

AspectWork From Home Data EngineerData Analyst
Required CredentialsBachelor's in CS, Data Engineering certificationsBachelor's in Statistics, Data Analysis certifications
Work EnvironmentRemote, technical teams, cloud platformsRemote or on-site, business teams, reporting tools
Employer & Industry UsageTech, finance, healthcare companiesMarketing, retail, finance sectors

Work From Home Data Engineers focus on building data pipelines and managing data infrastructure, requiring technical skills and cloud platform experience. Data Analysts interpret data to generate insights, often using reporting tools. While both roles can be remote and involve data, their core responsibilities and skill sets differ significantly.

What are popular job titles related to Work From Home Data Engineer jobs in Oregon? For Work From Home Data Engineer jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Work From Home Data Engineer jobs in Oregon look for? The top searched job categories for Work From Home Data Engineer jobs in Oregon are:
What cities in Oregon are hiring for Work From Home Data Engineer jobs? Cities in Oregon with the most Work From Home Data Engineer job openings:
Work From Home Data Engineer jobs near you

Data Engineer

Tebra

OR • Remote

$114K - $137K/yr

Full-time

Posted 12 days ago

Job description

About the Role

As a Data Engineer focused on AI/ML, you'll build, maintain, and optimize the data infrastructure that powers Tebra's intelligent features. You'll partner closely with Machine Learning Engineers, Data Scientists, and Software Engineers to transform complex healthcare data into high-quality datasets and real-time features that enable machine learning models.

This is a hands-on engineering role where you'll contribute to scalable data pipelines, improve data quality, and help ensure our AI systems are powered by reliable, performant, and well-governed data. You'll work on modern data platforms and gain experience building solutions that support both model training and production inference.

Your Area of Focus
  • Design, build, and maintain scalable data pipelines for feature extraction, training data generation, and model monitoring.
  • Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies.
  • Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness.
  • Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation.
  • Build reusable data engineering components, including automated data quality checks, schema validation, and testing frameworks.
  • Translate business requirements into scalable data solutions that enable analytics and machine learning use cases.
  • Optimize SQL queries, Spark workloads, and data processing pipelines to improve performance and scalability.
  • Collaborate with ML Engineers and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility.
  • Break down technical work into manageable tasks and deliver high-quality solutions within an agile team.
Your Professional Qualifications
  • 3+ years of professional experience in Data Engineering, Software Engineering, or a related field.
  • 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads.
  • Strong proficiency in Python and SQL with experience developing production-quality data pipelines.
  • Experience with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms.
  • Experience working with cloud-based data platforms such as Databricks, Snowflake, Delta Lake, or equivalent lakehouse technologies.
  • Understanding of data modeling, data warehousing, and data governance best practices.
  • Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts.
  • Experience deploying and supporting production data pipelines with monitoring, testing, and CI/CD practices.
  • Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams.
  • Excellent communication skills and a desire to continuously learn new technologies and engineering practices.

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