Unclassified - Richmond, VA

posted about 2 months ago

Full-time
Richmond, VA

About the position

We are seeking a skilled Python Developer with experience in PySpark and AWS to join our team. The ideal candidate will have a strong background in developing and optimizing data pipelines, working with large datasets, and leveraging AWS services to deliver scalable solutions. You will collaborate with data engineers, data scientists, and other developers to create efficient and reliable systems.

Responsibilities

  • Design, develop, and maintain data pipelines and ETL processes using Python, PySpark, and AWS services.
  • Work with large datasets to build and optimize data workflows and data processing pipelines.
  • Collaborate with cross-functional teams to understand business requirements and translate them into scalable and efficient data solutions.
  • Implement, monitor, and optimize cloud-based infrastructure and workflows using AWS services such as S3, Glue, EMR, Redshift, Lambda, and EC2.
  • Write clean, scalable, and efficient code following best practices in software engineering.
  • Perform debugging, testing, and troubleshooting of code to ensure high-quality and bug-free results.
  • Collaborate with data scientists and analysts to deliver data-driven solutions and models.
  • Ensure data security, integrity, and compliance with company and industry standards.

Requirements

  • Strong proficiency in Python programming with at least 3+ years of hands-on experience.
  • Experience with PySpark for distributed data processing and transformations.
  • Expertise in AWS services, including S3, Glue, Lambda, Redshift, EMR, and EC2.
  • Experience with ETL pipelines, data ingestion, transformation, and data modeling.
  • Familiarity with data warehousing concepts and working with structured and unstructured datasets.
  • Strong understanding of SQL and relational databases.
  • Experience in version control using Git and CI/CD pipelines.
  • Ability to work in a fast-paced, collaborative team environment.
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