Senior Data & Full-Stack Engineer (Python/AWS)

Pearson; Little Rock (AR); On-site; USD 80,000 - 90,000; Full time

Job summary

Pearson is seeking a Data Engineer / Full Stack Engineer to design and maintain scalable backend services and modern frontend interfaces. You will work with Python, Django/DRF, Nuxt/Vue, and AWS serverless to enable data pipelines and APIs across enterprise platforms.

The role emphasizes data transformation, secure authentication, and distributed Agile collaboration. Strong experience with AWS Glue, Pandas, and PostgreSQL is preferred.

Qualifications

• Hands-on Django/DRF, Nuxt/Vue, AWS serverless, and data transformation workflows.
• Strong experience building scalable APIs, data pipelines, and cloud-native services.
• Proficient with OpenAPI documentation, Pydantic validation, and secure auth.
• Comfortable in distributed Agile environments.

Responsibilities

• Design, develop, and maintain scalable backend services in Python/Django.
• Build RESTful APIs with DRF/OpenAPI; document APIs for teams.
• Develop and optimize data pipelines using AWS Glue, Pandas, and NumPy.
• Implement event-driven architectures with SQS/SNS/Lambda and deploy with CI/CD.

Skills

Python

Django/DRF

AWS

Frontend frameworks

Data engineering

API design

Security/auth

CI/CD

PostgreSQL

Serverless

Tools

Django REST Framework

OpenAPI/Swagger

Pydantic

django-rest-knox

OAuth

MSAL/Azure AD

Nuxt

Vue

Pinia

Tailwind CSS

Nuxt UI

Pandas

NumPy

AWS Glue

AWS CDK (Python)

Terraform

Docker

ECR

GitHub Actions

Redis

ElastiCache

PostgreSQL

Mangum

S3/Lambda/EventBridge

IAM/Secrets Manager

WAF

Job description

Pearson is seeking a Data Engineer / Full Stack Engineer to design and maintain scalable backend services and modern frontend interfaces. You will work with Python, Django/DRF, Nuxt/Vue, and AWS serverless to enable data pipelines and APIs across enterprise platforms.

The role emphasizes data transformation, secure authentication, and distributed Agile collaboration. Strong experience with AWS Glue, Pandas, and PostgreSQL is preferred.