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Junior Data Engineering Jobs in Seattle, WA (NOW HIRING)

Amazon

Data Engineer II, Data Management Team

Seattle, WA · On-site

$130K - $156K/yr

Mentor junior data engineers. Manage AWS resources including EC2, Redshift, S3, etc. Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency. Participating ...

Amazon

Data Engineer II, Data Management Team

Seattle, WA · Remote

$130K - $156K/yr

Mentor junior data engineers. Manage AWS resources including EC2, Redshift, S3, etc. Explore and ... learn the latest AWS technologies to provide new capabilities and increase efficiency.

Amazon

Data Engineer II, Data Management Team

Seattle, WA · Remote

$130K - $156K/yr

Mentor junior data engineers. Manage AWS resources including EC2, Redshift, S3, etc. Explore and ... learn the latest AWS technologies to provide new capabilities and increase efficiency.

Amazon

Data Engineer III - AMZ10212104

Seattle, WA · On-site

$162K - $209K/yr

Mentor junior data engineers. Position Requirements: Master's degree or foreign equivalent degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field and three ...

Staffingine LLC

Data Engineer

Bellevue, WA · On-site

$129K - $155K/yr

Implement data governance quality and security best practices across all data engineering processes ... Mentor junior engineers and contribute to architectural decisions and code reviews * Participate in ...

Amaze Systems Inc

Retail Data Architect

Issaquah, WA · On-site

$73.50 - $94.50/hr

Reverse engineer existing data models and transform them into future-state architectures aligned ... Guide and mentor junior data modelers and architects within an onsite-offshore delivery model.

CYNET SYSTEMS

Retail Data Architect

Issaquah, WA · On-site

$65 - $70/hr

Reverse engineer existing data models and transform them into future-state architectures aligned ... Guide and mentor junior data modelers and architects within an onsite-offshore delivery model.

Russell Investments

Data Platform Engineer II

Seattle, WA · On-site

$105K - $130K/yr

Your role will also involve mentoring junior engineers, promoting best practices, and driving continuous improvement in data engineering standards and platform reliability. Your Core Responsibilities:

Russell Investments

Data Platform Engineer II

Seattle, WA · On-site

$105 - $130/hr

Your role will also involve mentoring junior engineers, promoting best practices, and driving continuous improvement in data engineering standards and platform reliability. Core Responsibilities

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Showing results 1-20

All JobsData Engineering JobsJunior Data Engineering JobsJunior Data Engineering Jobs in Seattle, WA

Junior Data Engineering information

See Seattle, WA salary details

$38.1K

$81.7K

$124.6K

How much do junior data engineering jobs pay per year?

As of Aug 20, 2026, the average yearly pay for junior data engineering in Seattle, WA is $81,710.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,200.00 and $91,000.00 per year, depending on experience, location, and employer.

What does a junior data engineer do?

A Junior Data Engineer typically assists in designing, building, and maintaining data pipelines and databases to support analytics and business needs. They work with large datasets, ensuring data is collected, stored, and processed efficiently and accurately. Responsibilities often include data cleaning, ETL (Extract, Transform, Load) processes, and collaborating with data analysts and other engineers. Junior Data Engineers are usually early in their careers and work under the guidance of more experienced data engineers while developing their technical and problem-solving skills.

What are the key skills and qualifications needed to thrive as a junior data engineer?

To thrive as a Junior Data Engineer, you need a solid understanding of data structures, SQL, and programming languages like Python or Java, often supported by a degree in computer science or a related field. Familiarity with ETL tools, cloud platforms (such as AWS or Azure), and data warehousing solutions is typically required. Strong problem-solving abilities, attention to detail, and effective communication help you excel in team environments and manage complex data workflows. These skills ensure you can reliably build, maintain, and optimize data pipelines that support organizational decision-making.

What are some typical challenges a junior data engineer may face when starting out, and how can they overcome them?

As a Junior Data Engineer, one common challenge is adapting to complex data infrastructure and unfamiliar tools or frameworks. You may also find it challenging to ensure data quality and consistency while working with large datasets. Collaborating closely with senior engineers and asking questions is key to overcoming these hurdles. Taking advantage of onboarding resources, documentation, and code reviews will help you learn best practices and improve your skills quickly. Embracing continuous learning and seeking feedback will set you up for long-term growth in data engineering.

What is the difference between Junior Data Engineering vs Data Analyst?

AspectJunior Data EngineeringData Analyst
Required SkillsBasic SQL, Python, data pipeline knowledgeData visualization, SQL, Excel
CertificationsEntry-level certifications in data engineering or related fieldsCertifications in data analysis or visualization tools
Work EnvironmentData engineering teams, IT departmentsBusiness units, marketing, finance teams
Industry UsageBuilding and maintaining data pipelines and infrastructureInterpreting data, creating reports and dashboards

Junior Data Engineering focuses on developing and maintaining data pipelines and infrastructure, requiring skills in SQL and Python. Data Analysts interpret data and create reports, often using visualization tools. While both roles work with data, Junior Data Engineers handle data flow and storage, whereas Data Analysts focus on data interpretation and insights.

What are the most commonly searched types of Data Engineering jobs in Seattle, WA?

The most popular types of Data Engineering jobs in Seattle, WA are:

What are popular job titles related to Junior Data Engineering jobs in Seattle, WA?

For Junior Data Engineering jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Junior Data Engineering jobs in Seattle, WA look for?

The top searched job categories for Junior Data Engineering jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Junior Data Engineering jobs?

Cities near Seattle, WA with the most Junior Data Engineering job openings:

Junior Data Engineering jobs near you
Infographic showing various Junior Data Engineering job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $81,710 per year, or $39.3 per hour.

Senior Data Scientist

Applexus Technologies (P) Ltd

Federal Way, WA • On-site

$120 - $150/hr

Other

Posted 15 days ago

Job description

We are seeking an experienced and highly motivated Senior Data Scientist to design, develop, and scale advanced machine learning solutions that deliver measurable business outcomes. The ideal candidate will possess deep expertise across classical machine learning techniques, architect end-to-end ML pipelines, and build production-grade AI/ML solutions at scale.

This role requires close collaboration with business stakeholders, analytics teams, data engineers, and technology leaders to solve complex business problems across domains such as Finance and Supply Chain.

Key Responsibilities
  • Work closely with business stakeholders to identify opportunities and translate business challenges into AI/ML solutions.
  • Design, develop, and deploy machine learning models using structured and unstructured data.
  • Apply advanced statistical and machine learning techniques including regression, classification, clustering, recommendation systems, and time-series forecasting.
  • Architect and implement end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
  • Build scalable and production-ready ML systems using cloud-native technologies and MLOps best practices.
  • Drive model deployment through Docker, CI/CD pipelines, cloud-based serving infrastructure, and automated monitoring frameworks.
  • Establish robust monitoring mechanisms for model performance, data quality, drift detection, and retraining strategies.
  • Evaluate trade-offs between model accuracy, latency, scalability, and operational costs to deliver optimal business solutions.
  • Partner with data engineering teams to develop scalable data pipelines and feature stores.
  • Mentor junior data scientists and provide technical leadership on AI/ML initiatives.
  • Present findings, recommendations, and technical solutions to both business and executive stakeholders.
Qualifications Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

Experience

8+ years of experience in Data Science, Machine Learning, or Artificial Intelligence.

Demonstrated success in developing and deploying production-grade machine learning systems.

Technical Skills

Deep expertise in classical machine learning techniques including:

  • Regression
  • Classification
  • Time-Series Forecasting
  • Strong understanding of statistics, probability, experimentation, and predictive modeling.
Programming & Frameworks

Proficiency in Python and SQL.

Experience with machine learning frameworks such as:

  • Scikit-learn
  • XGBoost
  • TensorFlow
  • PyTorch
MLOps & Deployment

Experience designing and implementing end-to-end ML pipelines.

Strong hands‑on experience with:

  • Docker
  • Model serving and deployment frameworks
  • Drift monitoring and model lifecycle management

Experience with cloud platforms such as AWS, Azure, or GCP.

Strong understanding of data architecture, ETL processes, APIs, and large-scale data processing.

Experience working with structured, semi-structured, and unstructured datasets.

Preferred Qualifications
  • Functional knowledge in at least one business domain: Finance
  • Supply Chain
  • Experience with Generative AI and Large Language Models (LLMs) is a plus.
  • Experience working in consulting or customer‑facing environments.
  • Strong solution architecture and system design capabilities.
  • Ability to architect AI/ML solutions at scale.
  • Expertise in balancing accuracy, latency, scalability, and cost considerations.
  • Excellent communication and stakeholder management skills.
  • Strong analytical thinking and problem‑solving abilities.
  • Ability to lead cross‑functional teams and mentor junior team members.
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