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Data Engineer Jobs in Colorado (NOW HIRING)

Symhas

GCP DATA ENGINEER

Denver, CO · On-site

$117K - $141K/yr

GCP DATA ENGINEER Contract * Denver, Colorado * On-site / Hybrid Position Type Contract (1099 / C2C) Duration 6-12 Months (with extension potential) Location Denver, Colorado --5 days on-site Start ...

Viasat

Data Engineer

Englewood, CO · On-site

$113K - $135K/yr

They are seeking a Data Engineer to build infrastructure and tools for data collection and processing, enabling quicker insights and supporting data scientists and analysts. Responsibilities : • ...

Janus Henderson Investors

Data Engineer

Denver, CO · On-site

$90K - $115K/yr

  • Medical

  • Retirement

Your opportunity As a Data Engineer, you will build and maintain reliable data pipelines and solutions on the Janus Henderson Data Platform. You will work closely with team members and stakeholders ...

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All JobsData Engineer JobsData Engineer Jobs in Colorado

Data Engineer information

See Colorado salary details

$46.8K

$136.4K

$186.6K

How much do data engineer jobs pay per year?

As of Aug 14, 2026, the average yearly pay for data engineer in Colorado is $136,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,400.00 and $144,600.00 per year, depending on experience, location, and employer.

What is the difference between Data Engineer vs Data Scientist?

AspectData EngineerData Scientist
Primary FocusBuilding and maintaining data pipelines and infrastructureAnalyzing data to extract insights and create models
SkillsSQL, ETL, programming (Python, Java), database managementStatistics, machine learning, data analysis, programming (Python, R)
Work EnvironmentData warehouses, cloud platforms, backend systemsData analysis environments, research labs, visualization tools
Common ToolsApache Spark, Hadoop, Airflow, SQLJupyter, RStudio, Tableau, scikit-learn

Data Engineers focus on creating and maintaining the infrastructure that allows data to be collected, stored, and processed efficiently. Data Scientists analyze this data to generate insights, build predictive models, and support decision-making. While their skills overlap, Data Engineers are more involved in data pipeline development, whereas Data Scientists focus on data analysis and modeling.

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

To thrive as a Data Engineer, you need a strong background in computer science, data modeling, and programming languages such as Python or Java, often coupled with a relevant degree. Familiarity with ETL tools, big data frameworks (like Hadoop or Spark), and cloud platforms (such as AWS or Azure) is typically required, along with certifications like AWS Certified Data Analytics. Strong problem-solving skills, attention to detail, and effective communication set exceptional data engineers apart. These skills and qualities are essential for building robust data pipelines, ensuring data quality, and supporting data-driven decision-making across organizations.

What does a data engineer do?

The job duties of a data engineer involve helping with the development of systems, software, and infrastructure used to process, store and analyze data. Your responsibilities in this career include working to install data management software. Your employer may expect you to perform maintenance and install updates to all software and systems that they use for data acquisition, management, and analysis. Data engineers also analyze existing data systems to find ways to improve efficiency and accessibility. You then suggest upgrades or changes based on your assessment.

What is a data engineer?

Data Engineers are IT professionals who design, construct, install, and maintain large-scale processing systems and other infrastructure for collecting, storing, and analyzing data. They build and optimize data pipelines and architectures that allow organizations to efficiently access and use data for business insights. Data Engineers work closely with data scientists, analysts, and other stakeholders to ensure that data is reliable, accessible, and secure. Their responsibilities often include working with databases, cloud platforms, and big data tools.

How do data engineers typically collaborate with data scientists and analysts within an organization?

Data Engineers play a crucial role in ensuring that Data Scientists and Analysts have reliable, well-structured data for their projects. This collaboration often involves building and maintaining data pipelines, optimizing data storage solutions, and troubleshooting data quality issues. Regular communication and agile teamwork are common, with Data Engineers frequently participating in meetings to understand analytical requirements and adjust data processes accordingly. By working closely together, these teams can quickly iterate on data models and deliver actionable insights to drive business decisions.

Is a data engineer entry level?

Data engineering is typically an intermediate to senior-level role that requires experience with programming, databases, and data pipelines. Entry-level positions may be available for those with relevant internships or strong foundational skills, but most data engineering roles demand several years of experience and proficiency with tools like SQL, Python, and cloud platforms.
More about Data Engineer jobs

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Cities in Colorado with the most Data Engineer job openings:

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Data Engineer jobs near you
Infographic showing various Data Engineer job openings in Colorado as of August 2026, with employment types broken down into 77% Full Time, and 23% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $136,399 per year, or $65.6 per hour.

AWS Data Engineer | Big Data & Cloud Data Engineer

Long Finch Technologies

Littleton, CO • On-site

$114K - $137K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

We are seeking an experienced AWS Data Engineer / Big Data Technology Lead to design, develop, and maintain scalable data solutions using AWS cloud technologies and big data frameworks. The ideal candidate will have strong experience building data pipelines, managing data platforms, and delivering enterprise-level analytics solutions.

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines and architectures on AWS to support analytics, reporting, and operational workflows.
  • Develop ETL/ELT solutions using AWS services.
  • Build and manage cloud-based data lakes and data warehouse solutions using: Amazon S3, Amazon Athena, Amazon Redshift and Amazon RDS.
  • Design and implement scalable data architectures while ensuring data quality, security, and integrity.
  • Develop data processing workflows to support large-scale data ingestion and transformation.
  • Collaborate with data scientists, analysts, and business teams to curate and optimize production-ready datasets.
  • Monitor, troubleshoot, and improve data pipeline performance and reliability.
  • Work with DevOps tools and practices including Jenkins and Maven for deployment automation.
Required Experience & Skills
  • 7+ years of experience in Big Data Engineering, Data Engineering, or related roles.
  • Strong hands-on experience with AWS cloud services and Big Data technologies.
  • Experience designing, developing, and maintaining scalable data pipelines and cloud-based data architectures.
  • Proficiency in developing ETL/ELT pipelines using AWS services such as AWS Glue, Lambda, Kinesis, and Step Functions.
  • Experience building and managing data lakes and data warehouses using AWS services including S3, Athena, Redshift, and RDS.
  • Strong knowledge of data modeling, data integration, and ensuring data quality and integrity.
  • Experience working with Hadoop and other Big Data technologies.
  • Strong SQL skills with experience in databases such as Oracle 10g/11g/12c and SQL Server.
  • Experience with Unix/Linux environments and scripting.
  • Familiarity with DevOps tools such as Jenkins and Maven.
  • Experience with monitoring and logging tools such as Splunk is preferred.
  • Ability to collaborate with data scientists, analysts, and cross-functional teams to deliver production-ready data solutions.

Apply