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Evening Amazon Data Science Jobs in Colorado (NOW HIRING)

Amazon

Software Development Engineer II, DC Bridge

Denver, CO · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Our services directly contribute to the high availability of AWS data centers and Amazon ... science or equivalent PREFERRED QUALIFICATIONS - 3+ years of full software development life cycle ...

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Evening Amazon Data Science information

What are some common challenges faced by data scientists working evening shifts at Amazon, and how can they be managed?

Data scientists working evening shifts at Amazon may face challenges such as coordinating with colleagues in different time zones, maintaining effective communication with daytime teams, and managing work-life balance. To overcome these hurdles, it's helpful to leverage collaborative tools like Slack or Amazon Chime for asynchronous communication, schedule overlap meetings when possible, and establish clear expectations with team members. Additionally, evening shift roles can offer the advantage of uninterrupted focus time for deep analysis and model development, which can contribute to higher productivity and skill growth.

What is the difference between Evening Amazon Data Science vs Amazon Data Analyst?

AspectEvening Amazon Data ScienceAmazon Data Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fields; programming skills in Python/RBachelor's degree in Data Analysis, Business, or related fields; proficiency in Excel, SQL
Work EnvironmentFocus on developing models, algorithms, and advanced analytics during evening shiftsData reporting, visualization, and supporting business decisions, often during regular hours
Employer & Industry UsageUsed in tech and e-commerce sectors for machine learning and predictive modelingCommon in retail, e-commerce, and logistics for data reporting and insights

While both roles involve working with data at Amazon, Evening Amazon Data Science focuses on advanced analytics and model development during evening hours, whereas Amazon Data Analysts primarily handle data reporting and insights during regular hours. The roles differ in technical complexity and daily responsibilities but share a common goal of leveraging data to improve business outcomes.

What are the key skills and qualifications needed to thrive as an evening Amazon data science professional, and why are they important?

To thrive as an Evening Amazon Data Science professional, you need a strong background in statistics, machine learning, and data analysis, typically supported by a relevant degree in computer science, mathematics, or a related field. Proficiency with tools like Python, SQL, AWS services (such as Redshift or S3), and data visualization platforms is essential, along with experience using version control systems. Strong communication skills, problem-solving abilities, and adaptability to work independently during non-standard hours help you stand out in this role. These skills ensure you can effectively derive insights, collaborate across teams asynchronously, and support data-driven decision-making in Amazon’s dynamic environment.

What is an evening Amazon data science job?

An Evening Amazon Data Science job typically involves working as a data scientist at Amazon during evening hours, either as part of a flexible schedule or to cover specific business needs. Data scientists at Amazon analyze large datasets, develop predictive models, and provide insights to improve products, services, or operations. Working evening shifts may be ideal for those seeking non-traditional hours or balancing other commitments. Responsibilities are similar to daytime roles but may require additional collaboration with global teams or support for time-sensitive projects.

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:

Evening Amazon Data Science jobs near you

AWS Data Engineer | Big Data & Cloud Data Engineer

Long Finch Technologies

Indian Hills, CO

$114K - $137K/yr

Full-time

Posted 15 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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