Senior Data Engineer - USA

Coderio•Washington, DC

About The Position

Coderio is seeking a Senior Data Engineer based in the United States to design, build, and maintain robust data pipelines and scalable cloud infrastructure. The role supports Data Products throughout their lifecycle, including sourcing data through batch and stream processing, optimizing data architecture, and ensuring data quality. The engineer will work closely with Data Scientists and ML Engineers to automate workflows and deploy resilient production pipelines. The position is US-based and may be remote or hybrid. Candidates must be authorized to work in the United States. The technology environment includes AWS services (Redshift, Glue, EMR, MWAA, and S3) and GCP (BigQuery and Cloud Storage), along with Python, PySpark, advanced SQL, Docker, Terraform, CI/CD pipelines, UNIX/Linux environments, and MLOps practices. Coderio describes itself as remote-first and offers an inclusive, challenging environment with opportunities for growth and fair compensation.

Requirements

At least 5 years of experience in Data Engineering, with a track record of designing and operating large-scale data systems.
Mastery of Python and advanced SQL, including query optimization and performance tuning.
Hands-on experience with AWS services (Redshift, Glue, EMR, MWAA, S3) or GCP (BigQuery), and modern data processing engines such as PySpark.
Proficiency with UNIX environments, Docker, Git, and CI/CD pipelines.
Solid understanding of data structures and batch and streaming processing; familiarity with MLOps or the ML deployment lifecycle.
Excellent verbal and written English communication skills.
Must be authorized to work in the United States.

Nice To Haves

Experience with Infrastructure as Code, such as Terraform.
Experience building and maintaining scalable REST APIs.
Exposure to prompt engineering workflows or generative AI data pipelines.

Responsibilities

Design, build, and deploy scalable, decoupled, high-performance data pipelines for batch and streaming ingestion into Data Lakes and Data Warehouses.
Develop and optimize ETL/ELT pipelines using Python, PySpark, and advanced SQL.
Collaborate with Data Scientists and ML Engineers to build resilient pipelines, automate MLOps workflows, and gather model interaction data for retraining loops.
Monitor, troubleshoot, and optimize pipeline execution, query performance, and storage resource utilization across cloud platforms.
Implement data quality frameworks and testing controls to support data integrity, reliability, and governance across raw and curated datasets.
Apply MLOps and DevOps best practices, maintain CI/CD pipelines, use Docker for containerization, and document technical standards and architectures.