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Temporary Amazon Data Engineer Jobs (NOW HIRING)

The Maven Group, LLC

Data Engineer

Suitland, MD · On-site

$123K - $148K/yr

Data Engineer We are looking for a skilled and passionate Data Engineer to join our team. You will ... Amazon Redshift, or BigQuery. * Big Data Processing: Leverage frameworks like Apache Spark ...

New

The Maven Group, LLC

Data Engineer

Suitland, MD · On-site

$123K - $148K/yr

Data Engineer We are looking for a skilled and passionate Data Engineer to join our team. You will ... Amazon Redshift, or BigQuery. * Big Data Processing: Leverage frameworks like Apache Spark ...

New

Amazon

Data Engineer, WWASFT

Seattle, WA · On-site

$130K - $156K/yr

WorldWide Amazon Stores FinTech (WWASFT) team is looking for an Data Engineer who is data-driven, uncompromisingly detail oriented, smart, efficient, and driven to help our business succeed. You have ...

System One

Data Engineer

Tulsa, OK · On-site

$99K - $119K/yr

Amazon Kinesis * IAM and AWS Security best practices * Advanced SQL skills and experience ... AWS Certified Data Engineer, AWS Certified Solutions Architect, or similar certification.

New

Accenture Federal Services

Data Engineer

Suitland, MD · On-site

$123K - $148K/yr

They are seeking a skilled Data Engineer to design, build, and maintain data infrastructure ... Amazon Redshift, or BigQuery. • Leverage frameworks like Apache Spark, Databricks, or Apache ...

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Temporary Amazon Data Engineer information

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$44.5K

$129.7K

$177.5K

How much do temporary amazon data engineer jobs pay per year?

As of Sep 13, 2026, the average yearly pay for temporary amazon data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What does a temporary Amazon data engineer do?

A Temporary Amazon Data Engineer is responsible for designing, building, and maintaining data pipelines and systems within Amazon on a short-term or contract basis. They work with large datasets, ensure data quality, and collaborate with other teams to support analytics and business intelligence initiatives. Their role includes tasks such as data extraction, transformation, and loading (ETL), as well as troubleshooting data issues and optimizing data workflows. Although the position is temporary, it often requires strong technical skills in SQL, Python, and cloud services like AWS. The work helps Amazon make data-driven decisions and improve its services.

What are the key skills and qualifications needed to thrive as a temporary Amazon data engineer?

To thrive as a Temporary Amazon Data Engineer, you need strong proficiency in data modeling, SQL, and programming languages such as Python or Java, typically supported by a degree in computer science or a related field. Familiarity with Amazon Web Services (AWS) data tools like Redshift, S3, Glue, and ETL pipelines, as well as relevant certifications like AWS Certified Data Analytics, is highly valuable. Excellent problem-solving skills, adaptability, and effective communication are crucial soft skills for collaborating on short-term projects and delivering timely results. These skills and qualifications ensure efficient data solutions, seamless integration with Amazon's platforms, and the ability to meet dynamic business needs in a fast-paced environment.

What are some common challenges temporary Amazon data engineers face when onboarding to new projects?

Temporary Amazon Data Engineers often encounter challenges such as quickly adapting to existing data infrastructure, understanding proprietary tools and processes, and integrating with teams that may already have established workflows. Since the role is time-limited, it’s crucial to rapidly build relationships with stakeholders and clarify project goals early on. Proactive communication and leveraging available documentation can help overcome these hurdles, ensuring a smoother transition and effective project delivery.

What is the difference between Temporary Amazon Data Engineer vs Temporary Google Data Engineer?

AspectTemporary Amazon Data EngineerTemporary Google Data Engineer
Required CredentialsBachelor's in CS, Data Engineering certifications, AWS knowledgeBachelor's in CS, Data Engineering certifications, GCP knowledge
Work EnvironmentAmazon's cloud infrastructure, e-commerce, and logisticsGoogle Cloud Platform, advertising, and tech services
Employer & Industry UsageAmazon, retail, logistics, cloud servicesGoogle, tech, advertising, cloud services
Search & Comparison IntentHigh overlap in cloud data roles, certifications, and industrySimilar roles in cloud data engineering, but with different platform focus

Temporary Amazon Data Engineers and Temporary Google Data Engineers share similar skills, certifications, and work environments focused on cloud data platforms. The main difference lies in the cloud platform expertise—AWS for Amazon and GCP for Google—making each role suited to specific employer ecosystems and industry applications.

More about Temporary Amazon Data Engineer jobs

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Temporary Amazon Data Engineer jobs near you
Infographic showing various Temporary Amazon Data Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Engineer

Suitland, MD • On-site

The Maven Group, LLC
See more jobs at The Maven Group, LLCexternal

$123K - $148K/yr

Full-time

Posted 2 days ago

New

Job description

Data Engineer

We are looking for a skilled and passionate Data Engineer to join our team. You will play a critical role in designing, building, and maintaining our data infrastructure to ensure seamless data flow, scalability, and reliability. You will work closely with data scientists, analysts, and other stakeholders to develop efficient data pipelines, manage large datasets, and integrate machine learning models into production environments.

Key Responsibilities:

  • Programming Fundamentals: Write clean, efficient, and scalable code to build and optimize data solutions using programming languages like Python.
  • Data Pipeline Development: Design, build, and orchestrate robust and reliable data workflows using tools such as Apache Airflow, dbt, Prefect, or Dagster.
  • Cloud Platform Familiarity: Work comfortably in cloud environments, with a strong preference for experience in AWS. Experience in GCP or Azure is also highly valued.
  • Database & Querying Skills: Extract, integrate, and ensure the quality of data from various sources using tools and technologies such as SQL, PostgreSQL, Snowflake, Amazon Redshift, or BigQuery.
  • Big Data Processing: Leverage frameworks like Apache Spark, Databricks, or Apache Kafka to process and manage large-scale data workflows with reliability and efficiency.
  • ML Integration / MLOps: Support the implementation, deployment, and scaling of machine learning models in production environments using tools like Amazon SageMaker, MLflow, or Kubeflow.
  • Monitoring & Troubleshooting: Monitor data pipeline health, troubleshoot issues, and ensure data consistency using tools such as Amazon CloudWatch, Datadog, or Great Expectations.
  • Collaboration & Documentation: Work closely with data scientists, analysts, and other stakeholders to understand data requirements, communicate solutions, and document processes using tools like Git, Jira, and Confluence.

Qualifications:

  • 2 years of experience as a Data Engineer or similar role.
  • Strong proficiency in Python or other programming languages relevant to data engineering.
  • Any additional experience with any of the following:
    • Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, dbt, Prefect, Dagster).
    • Solid understanding of cloud platforms (AWS strongly preferred; GCP or Azure experience also considered).
    • Expertise in SQL and familiarity with relational and columnar databases (e.g., PostgreSQL, Snowflake, BigQuery).
    • Knowledge of big data processing frameworks (e.g., Apache Spark, Databricks, or Apache Kafka).
    • Familiarity with machine learning workflows and experience implementing MLOps tools (e.g., Amazon SageMaker, MLflow, or Kubeflow) in production environments.
    • Strong troubleshooting skills and experience monitoring data pipelines and system health using tools like Amazon CloudWatch, Datadog, or Great Expectations.
    • Excellent communication skills and a collaborative mindset, with a focus on documentation and best practices.

Preferred Skills:

  • Experience working with large-scale distributed systems.
  • Knowledge of data governance and security best practices.
  • Proven ability to work in cross-functional teams and contribute to problem-solving and innovation.

Clearance:

  • An active TS/SCI federal security clearance is required

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