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Full Stack Data Engineer Jobs in Salt Lake City, UT

Enzo Health

Senior Full Stack Engineer

Lehi, UT · On-site

Delivers intake decisions in seconds, automatically extracting key data from referrals to increase ... About the role We're looking for a high-impact Senior Full Stack Engineer to help build and scale ...

Kavaliro

Full Stack Engineer

Salt Lake City, UT · On-site

The Full Stack Engineer at will work end-to-end on new features, enhancements, and defect resolution on one or more of software platforms. You will work closely with our Product Management team ...

Proofpoint

Full Stack Engineer

Draper, UT · On-site

We protect how people, data, and AI agents connect across email, cloud, and collaboration tools ... full-stack Software Engineer III on our Satori AI Platform team, you will be building the ...

Workstream

Software Engineer (Full Stack)

Lehi, UT · On-site

$100K - $140K/yr

We are seeking a talented Software Engineer (Full Stack) to join our growing engineering team. This ... market data. Know More About Workstream * * * Additional Information Workstream provides equal ...

Emerging Threats Pro

Full Stack Engineer

Draper, UT · On-site

We protect how people, data, and AI agents connect across email, cloud, and collaboration tools ... full-stack Software Engineer III on our Satori AI Platform team, you will be building the ...

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All JobsFull Stack Data Engineer JobsFull Stack Data Engineer Jobs in Salt Lake City, UT

Full Stack Data Engineer information

See Salt Lake City, UT salary details

$43.1K

$130.4K

$184.3K

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

As of Aug 25, 2026, the average yearly pay for full stack data engineer in Salt Lake City, UT is $130,419.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,400.00 and $152,900.00 per year, depending on experience, location, and employer.

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.

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 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.

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.

What are popular job titles related to Full Stack Data Engineer jobs in Salt Lake City, UT?

For Full Stack Data Engineer jobs in Salt Lake City, UT, the most frequently searched job titles are:

What job categories do people searching Full Stack Data Engineer jobs in Salt Lake City, UT look for?

The top searched job categories for Full Stack Data Engineer jobs in Salt Lake City, UT are:

What cities near Salt Lake City, UT are hiring for Full Stack Data Engineer jobs?

Cities near Salt Lake City, UT with the most Full Stack Data Engineer job openings:

Full Stack Data Engineer jobs near you
Infographic showing various Full Stack Data Engineer job openings in Salt Lake City, UT as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $130,419 per year, or $62.7 per hour.

Full-Stack Software Engineer, Platform Data

Jobtailor

Salt Lake City, UT • On-site

$120 - $180/hr

Other

Posted 7 days ago

Job description

  • Design and operate pipelines that ingest data from many sources - internal systems, purchased datasets, and external feeds - reconciling them into clean, well-modeled, findable data that people trust.
  • Build the web apps which deliver that data to stakeholders including dashboards, internal applications, and other interfaces.
  • Own features end to end: designing system architecture, pipelines, APIs, and frontends, while being accountable for ensuring the entire system runs smoothly.
  • Partner directly with teams across the organization to understand what they're trying to learn from the data, then build the thing that allows them to answer their questions.
Requirements
  • Full-stack builder: You've built and maintained backend data systems and their associated user-facing applications. You're fluent in Python and SQL on the backend and in TypeScript and React on the frontend. You own features spanning the whole path from source to screen.
  • Data engineering instincts: You've designed schemas and built pipelines that move data reliably from messy sources into clean, queryable form. You think about idempotency, data quality, and what happens when an upstream source changes. Experience with orchestration and warehouse tooling (e.g., Airflow, Dagster, dbt, Snowflake, or BigQuery) is a strong plus. Experience with geospatial or scientific data - raster/vector formats, large file stores, PostGIS, or similar - is also nice-to-have.
  • Product-minded and collaborative: You can sit with a geoscientist, watch where they get stuck, and translate that into shipped products. You treat stakeholders as partners, working comfortably across teams, keeping people in the loop, surfacing trade-offs early, and building alignment on what to build and why.
  • Cloud & infrastructure fluency: You're comfortable deploying and operating what you build on a public cloud (AWS or GCP), with containers (Kubernetes) and infrastructure-as-code (e.g., Pulumi and Terraform).
  • Self-directed and comfortable with unsolved problems: You research options and make recommendations, doing your best work when the problem is real and the constraints are hard. You leverage and delegate to AI, treating modern AI tools as a core part of how you work, handing off tasks to AI agents to use them as a force-multiplier.
Core Competencies

Demonstrates expertise in full-stack development, including backend data systems and user-facing applications, with a strong focus on data engineering, cloud infrastructure, and collaboration with stakeholders to deliver impactful solutions.

Highest-signal resume keywords
  • Python Programming
  • SQL Database Management
  • TypeScript Development
  • React Framework
  • Data Pipeline Design
ATS Optimization Keywords Hard Skills
  • Data Engineering
  • Schema Design
  • API Development
  • Frontend Development
  • Backend Development
  • Data Quality Assurance
  • Orchestration Tools
  • Cloud Deployment
  • Infrastructure as Code
  • Geospatial Data Handling
Soft Skills
  • Collaborative Problem Solving
  • Stakeholder Engagement
  • Self-Direction
  • Communication
  • Product Mindset
Industry Keywords
  • Data Ingestion
  • Data Modeling
  • Data Quality
  • Idempotency
  • Public Cloud
  • Containers
  • AI Tools
  • Geoscience
  • Raster Data
  • Vector Data
Tools & Technologies
  • AWS
  • GCP
  • Kubernetes
  • Airflow
  • Dagster
  • Dbt
  • Snowflake
  • BigQuery
  • Pulumi
  • Terraform
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