Senior Data Engineer - Cloud Pipelines & MLOps (Remote)

Coderio; New Jersey; Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Remote-first company

Fair compensation

Job summary

Coderio is seeking a Senior Data Engineer based in the United States to design, build, and maintain scalable data pipelines and data platforms. The role focuses on batch and streaming data ingestion into data lakes and warehouses, with strong emphasis on data quality and governance.

You will collaborate with Data Scientists and ML Engineers to deploy reliable pipelines, optimize performance, and support MLOps-driven workflows in a remote-first environment.

Qualifications

• 5+ years of experience in Data Engineering with proven large-scale data systems experience.
• Mastery of Python and advanced SQL (query optimization).
• Hands-on experience with AWS (Redshift, Glue, EMR, MWAA, S3) or GCP (BigQuery).
• Experience with PySpark and modern data processing engines.

Responsibilities

• Design, build, and maintain scalable batch and streaming data pipelines.
• Collaborate with Data Scientists and ML Engineers to enable ML workflows.
• Optimize performance and monitor pipeline execution across cloud platforms.
• Implement data quality frameworks and governance across datasets.
• Develop REST APIs and data services for data products.
• Collaborate on MLOps and deployment lifecycles.

Skills

Python

PySpark

SQL

ETL/ELT

Batch Processing

Stream Processing

Data Warehousing

Data Modeling

MLOps

REST APIs

CI/CD

Docker

Git

Unix/Linux

Tools

Docker

Terraform

Git

CI/CD pipelines

Job description

Coderio is seeking a Senior Data Engineer based in the United States to design, build, and maintain scalable data pipelines and data platforms. The role focuses on batch and streaming data ingestion into data lakes and warehouses, with strong emphasis on data quality and governance.

You will collaborate with Data Scientists and ML Engineers to deploy reliable pipelines, optimize performance, and support MLOps-driven workflows in a remote-first environment.