Senior Data Engineer — Remote (US) for Scalable Pipelines

Coderio; California; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote-first

Growth opportunities

Job summary

Coderio is hiring a Senior Data Engineer in the United States (Remote/Hybrid). You will design, build, and maintain scalable data pipelines across our cloud platform, enabling data products throughout their lifecycle.

Collaborate with Data Scientists and ML Engineers to automate workflows and deploy resilient data pipelines in production. Ideal candidates have 5+ years in data engineering, strong Python/SQL, and experience with AWS/GCP, PySpark, and MLOps practices.

Qualifications

• 5+ years of experience in Data Engineering with large-scale systems.
• Mastery of Python and advanced SQL (query optimization).
• Hands-on experience with AWS or GCP and modern data processing engines (PySpark).
• Unix/Linux environments, Docker, Git, and CI/CD pipelines familiarity.
• Solid understanding of data structures, batch/streaming processing, and ML lifecycle awareness.
• Excellent verbal and written English communication.

Responsibilities

• Pipeline Architecture & Development: design, build, and deploy scalable data pipelines into Data Lakes/Warehouses.
• ETL/ELT Processing: develop and optimize pipelines using Python, PySpark, and SQL.
• Collaboration with ML & Data Science: partner with DS/ML to build resilient pipelines and automate MLOps workflows.
• Performance Optimization & Monitoring: monitor and optimize pipeline execution, queries, and storage across cloud platforms.
• Data Quality & Governance: implement data quality frameworks and governance across datasets.
• DevOps & Standards: apply MLOps and DevOps best practices, maintain CI/CD pipelines, document standards.

Skills

Python

SQL

Communication

Teamwork

Tools

AWS

GCP

Docker

Terraform

MWAA

PySpark

Data Warehousing

Job description

Coderio is hiring a Senior Data Engineer in the United States (Remote/Hybrid). You will design, build, and maintain scalable data pipelines across our cloud platform, enabling data products throughout their lifecycle.

Collaborate with Data Scientists and ML Engineers to automate workflows and deploy resilient data pipelines in production. Ideal candidates have 5+ years in data engineering, strong Python/SQL, and experience with AWS/GCP, PySpark, and MLOps practices.