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Full Stack Data Engineer Jobs (NOW HIRING)

Cognition

Data Engineer

San Francisco, CA · On-site

$134.90K - $162K/yr

About the Role We're hiring a technical Data Engineer to own our full data stack - from database architecture and pipelines to instrumenting telemetry and owning integrations and reporting. You'll ...

Programmers.io

Full Stack Engineer

Seattle, WA · On-site

We are seeking a highly skilled Full Stack Developer with strong Data Engineering experience and proven expertise in API Development (must-have) to join our dynamic team. The ideal candidate will be ...

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How much do full stack data engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for full stack data engineer in the United States is $134,771.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $158,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Stack Data Engineer, and why are they important?

To thrive as a Full Stack Data Engineer, you need strong expertise in data modeling, ETL processes, and proficiency in both backend (e.g., Python, Java) and frontend (e.g., JavaScript, React) development, often supported by a degree in computer science or a related field. Familiarity with cloud platforms (such as AWS or Azure), big data tools (like Spark or Hadoop), and database systems (SQL and NoSQL) is typically required, and certifications in these technologies are advantageous. Excellent problem-solving, communication, and collaboration skills help you bridge gaps between data, development, and business teams. These skills ensure you can design, build, and maintain scalable data solutions that meet organizational needs efficiently.

How does a Full Stack Data Engineer typically balance responsibilities between backend data infrastructure and frontend data presentation tasks?

Full Stack Data Engineers are often required to split their time between developing robust backend data pipelines and creating user-facing tools or dashboards that visualize data insights. This dual responsibility means you'll need to prioritize tasks based on project needs, effectively collaborating with data scientists, analysts, and frontend developers. Communication is key, as you'll bridge gaps between technical teams and business stakeholders, ensuring data flows seamlessly from source systems to end users. Over time, many engineers find opportunities to specialize further or move into leadership roles overseeing data architecture and team strategy.

What is a Full Stack Data Engineer?

A Full Stack Data Engineer is a professional who designs, builds, and maintains the entire data pipeline, from data collection and storage to processing and visualization. They work with both the backend infrastructure (such as databases, data warehouses, and ETL processes) and frontend tools (like dashboards or reporting systems) to ensure data is accessible and usable for analytics. Full Stack Data Engineers possess skills in programming, database management, data modeling, cloud platforms, and often data visualization, allowing them to manage every stage of data flow within an organization.

What is the difference between Full Stack Data Engineer vs Data Scientist?

AspectFull Stack Data EngineerData Scientist
CredentialsBachelor's/Master's in CS, Data Engineering certificationsBachelor's/Master's in CS, Data Science or related fields
Work EnvironmentBuild data pipelines, manage databases, develop APIsAnalyze data, create models, generate insights
Industry UsageTech, finance, healthcare, where data infrastructure is keyResearch, analytics, product development teams

Full Stack Data Engineers focus on building and maintaining data infrastructure, integrating data from various sources, and ensuring data availability. Data Scientists analyze data, develop models, and generate insights. While both roles require strong technical skills, Full Stack Data Engineers are more involved in data pipeline development, whereas Data Scientists focus on data analysis and modeling.

More about Full Stack Data Engineer jobs
What cities are hiring for Full Stack Data Engineer jobs? Cities with the most Full Stack Data Engineer job openings:
What states have the most Full Stack Data Engineer jobs? States with the most job openings for Full Stack Data Engineer jobs include:
What job categories do people searching Full Stack Data Engineer jobs look for? The top searched job categories for Full Stack Data Engineer jobs are:
Full Stack Data Engineer jobs near you
Infographic showing various Full Stack Data Engineer job openings in the United States as of May 2026, with employment types broken down into 85% Full Time, 4% Part Time, 2% Temporary, and 9% Contract. Highlights an 75% In-person, 5% Hybrid, and 20% Remote job distribution, with an average salary of $134,771 per year, or $64.8 per hour.
Senior Data Engineer - Full Stack

Senior Data Engineer - Full Stack

Codvo.ai

Santa Clara, CA • On-site

Full-time

Posted 6 days ago

Job description

Role Summary
We are seeking a highly skilled Senior Data Engineer - Full Stack to build and maintain internal tools, automation frameworks, and workflows that enhance the efficiency, reliability, and scalability of our data and machine learning platforms. This role will work closely with Data Engineers, Data Scientists, and ML Engineers to streamline operations across the data lifecycle.
Total Exp: 8+Years
Location: Santa Clara, CA(Hybrid)
Key Responsibilities
  • Design and develop CLI tools, scripts, and internal utilities to automate repetitive tasks across the data platform, including:
    • Pipeline execution and orchestration
    • Data governance workflows
    • Metadata synchronization
    • Environment setup and configuration
    • Test harness development
  • Automate workflows on Databricks, including:
    • Job deployment and scheduling
    • Environment provisioning
    • MLOps processes using APIs, Terraform, or Databricks SDK
  • Build and implement robust testing frameworks:
    • Integration testing for pipelines
    • End-to-end validation of ETL/ELT workflows
    • Testing and validation for ML inference workflows
  • Improve overall productivity, scalability, and reliability of the data and ML engineering ecosystem
  • Develop lightweight internal tools and dashboards using frameworks such as React, Streamlit, or similar technologies to:
    • Visualize data pipelines and workflows
    • Demonstrate model inference capabilities
    • Provide configuration and operational controls
    • Enable internal productivity monitoring and dashboards
  • Collaborate with cross-functional teams to identify automation opportunities and implement best practices

Required Skills & Qualifications
  • Strong experience in Python and scripting for automation and backend development
  • Hands-on experience with Databricks platform and ecosystem
  • Experience with APIs, Terraform, and/or Databricks SDK for automation
  • Solid understanding of ETL/ELT pipelines and data platform architecture
  • Experience building testing frameworks for data pipelines and ML workflows
  • Familiarity with CLI tool development and system automation
  • Knowledge of MLOps principles and practices
  • Experience with modern development practices, including:
    • Spec-driven development
    • Use of coding agents or automation-assisted development tools
    • Version control and CI/CD pipelines

Nice to Have
  • Experience building dashboards or internal tools using React, Streamlit, or similar frameworks
  • Familiarity with Databricks AI/BI or other data visualization tools
  • Exposure to data governance and metadata management frameworks
  • Experience working with cloud platforms (AWS preferred)

Preferred Experience
  • 8+ years of experience in Data Engineering, Platform Engineering, or related roles
  • Experience working in data-driven or ML-focused environments

What You'll Bring
  • Strong problem-solving mindset with a focus on automation and efficiency
  • Ability to work in a fast-paced, collaborative environment
  • Passion for building scalable internal tools and improving developer productivity

Apply