20 days ago
Washington, DC, USA +3 moreSenior
Responsibilities
- Design, build, and deploy scalable batch and streaming data pipelines for data lakes and data warehouses.
- Develop and optimize ETL/ELT pipelines using Python, PySpark, and advanced SQL.
- Partner with data scientists and ML engineers to automate MLOps workflows and support model retraining loops.
- Monitor, troubleshoot, and optimize pipeline execution, query performance, and cloud storage utilization.
- Implement data quality frameworks, testing controls, and governance for raw and curated datasets.
- Apply DevOps and MLOps practices, maintain CI/CD pipelines, use Docker for containerization, and document technical standards and architectures.
Requirements
- Require 5+ years of data engineering experience designing and operating large-scale data systems.
- Require mastery of Python and advanced SQL, including query optimization and performance tuning.
- Require hands-on experience with AWS or GCP data services and modern processing engines such as PySpark.
- Require proficiency with UNIX environments, Docker, Git, and CI/CD pipelines.
- Require understanding of data structures, batch and streaming processing, and the ML deployment lifecycle or MLOps.
- Require excellent verbal and written English communication skills.
- Preferred experience includes Terraform, scalable REST APIs, prompt engineering workflows, or generative AI data pipelines.
Benefits
- US-based remote or hybrid work arrangement.
- Applicants must be authorized to work in the United States.
Tech Stack
Categories
Data Engineering
About Coderio
Coderio is a nearshore software development firm that provides staff augmentation and managed teams to build web, mobile, and cloud applications for global companies. Founded in 2017 and headquartered in New York, the privately held company assembles timezone-aligned engineering squads across the Americas to deliver end-to-end projects and platform modernization. Services span front- and back-end development, data and AI, cloud migrations, and legacy application modernization.