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Amazon Data Science Jobs in Colorado (NOW HIRING)

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Amazon Data Science information

See Colorado salary details

$48.4K

$173.5K

$256K

How much do amazon data science jobs pay per year?

As of Aug 21, 2026, the average yearly pay for amazon data science in Colorado is $173,519.00, according to ZipRecruiter salary data. Most workers in this role earn between $140,400.00 and $178,800.00 per year, depending on experience, location, and employer.

What is an Amazon data science?

An Amazon Data Science job involves leveraging data to drive business decisions, optimize operations, and enhance customer experiences. Data scientists at Amazon work with machine learning, statistical modeling, and big data technologies to analyze vast datasets and generate actionable insights. They collaborate with engineering, product, and business teams to develop data-driven solutions for challenges such as recommendation systems, demand forecasting, and fraud detection. Strong programming skills in Python or Scala, expertise in SQL, and experience with AWS tools are commonly required.

What types of projects and challenges can I expect as an Amazon data science team member?

As an Amazon Data Science team member, you can expect to work on projects ranging from optimizing supply chains and recommendation systems to improving customer experiences and forecasting demand. Daily responsibilities often involve analyzing large data sets, building predictive models, and collaborating closely with product managers, software engineers, and business leaders. The pace is fast, with opportunities to tackle complex problems that have a direct impact on Amazon’s customers and operations. You’ll also have the chance to grow your skills through cross-team projects, participation in internal workshops, and exposure to emerging data science technologies.

What are the key skills and qualifications needed to thrive in the Amazon data science position, and why are they important?

To thrive as an Amazon Data Science professional, you need strong analytical abilities, expertise in statistics and machine learning, and a solid educational background in computer science, mathematics, or a related field. Proficiency in programming languages such as Python or R, familiarity with big data tools like AWS, Spark, or Hadoop, and relevant certifications (e.g., AWS Certified Data Analytics) are often required. Effective communication, business acumen, and collaborative problem-solving set exceptional candidates apart. These skills are crucial for transforming complex data into actionable insights that drive impactful business decisions at Amazon.

Does Amazon have data science jobs?

Yes, Amazon offers data science jobs across various teams, focusing on areas such as machine learning, data analysis, and predictive modeling. These roles typically require skills in programming, statistics, and tools like Python, R, or SQL, and often involve working in collaborative, fast-paced environments. Candidates should review Amazon's careers page for current openings and specific role requirements.

What are the most commonly searched types of Amazon Data Science jobs in Colorado?

The most popular types of Amazon Data Science jobs in Colorado are:

What cities in Colorado are hiring for Amazon Data Science jobs?

Cities in Colorado with the most Amazon Data Science job openings:

Amazon Data Science jobs near you
Infographic showing various Amazon Data Science job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $173,519 per year, or $83.4 per hour.

AWS Data Engineer | Big Data & Cloud Data Engineer

Long Finch Technologies

Westminster, CO • On-site

$115K - $138K/yr

Full-time

Posted 7 days ago

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.

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