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Prefect Technologies Jobs (NOW HIRING)

Accenture Federal Services

Data Engineer

Suitland, MD · On-site

$123K - $148K/yr

... Airflow, dbt, Prefect, or Dagster. * Cloud Platform Familiarity : Work comfortably in cloud ... technologies such as SQL, PostgreSQL, Snowflake, Amazon Redshift, or BigQuery. * Big Data ...

Centro, Inc

Data Engineer

North Liberty, IA · On-site

$104K - $125K/yr

Information Technology Reports To: Principal Software Engineer Position Summary Centro is seeking a ... Orchestrate scheduled and on-demand workflows using Prefect. * Integrate data from ERP, ...

Lorven Technologies

DataOps & Build Engineer

Core Technology : Airflow, Dagster, Prefect, Monte Carlo, Acceldata * Cloud Environment: AWS (S3, IAM, VPC, etc.) * Ingest & Transform: dbt Core, AWS Glue Streaming/Integration: AWS Streaming ...

Lynker

Scientific Full Stack Developer

Boulder, CO · On-site

Working closely with the Web and Testbed teams as well as other developers in the Technology ... Build and maintain ETL pipelines and services using Prefect * Deploy applications to dedicated ...

Sierra Nevada Corporation

MLOps & Data Engineer

Englewood, CO · On-site

$113K - $136K/yr

Dream, Innovate, Inspire and Empower the next generation to transform humanity through technology ... Proficiency in SQL and experience with ETL/orchestration tools such as Airflow, dbt, or Prefect

Accenture Federal Services

Data Engineer

Suitland, MD

$123K - $148K/yr

Join Accenture Federal Services, a technology company within global Accenture. Recognized as a ... Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, dbt, Prefect ...

Form Energy

Staff Data Engineer

Somerville, MA · On-site

Form Energy is an American manufacturing and energy technology company. We're revolutionizing ... Build and maintain data orchestration (Dagster, Prefect, or Airflow) for workflows that are ...

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Prefect Technologies information

What is the difference between Prefect Technologies vs Data Engineer?

AspectPrefect TechnologiesData Engineer
Primary RoleDevelops and manages data workflows and automation toolsDesigns, builds, and maintains data pipelines and infrastructure
Required SkillsPython, workflow orchestration, automationSQL, Python, ETL processes, data modeling
Work EnvironmentTech companies, startups, cloud-based platformsData teams, analytics departments, tech firms
CertificationsNone specific, technical proficiency preferredData-related certifications (e.g., AWS, Google Cloud) beneficial

Prefect Technologies focuses on developing workflow orchestration tools to automate data processes, while Data Engineers build and maintain the data pipelines and infrastructure. Both roles require technical skills like Python and data handling, but Prefect roles are more centered on workflow management, whereas Data Engineers focus on data architecture and pipeline creation.

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Infographic showing various Prefect Technologies job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 91% Full Time, 5% Part Time, 2% Contract, and 1% Nights. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution.

Data Engineer

Suitland, MD • On-site

Accenture Federal Services
IT Services • 10K+ employees
Learn more about Accenture Federal Servicesexternal

$123K - $148K/yr

Full-time

Re-posted 4 days ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

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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