Senior Data Engineer - USA

Coderio; Williamstown (NJ); Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Remote-first culture

Fair compensation

Job summary

Coderio is seeking a Senior Data Engineer to design, build, and maintain robust data pipelines and scalable infrastructure across our cloud platform. You will source data via batch and streaming processes, optimize architecture, and ensure data quality while collaborating with Data Scientists and ML Engineers.

The role is US-based with remote or hybrid options, requiring authorization to work in the United States. Expect collaboration across international teams on data products and pipelines.

Qualifications

• 5+ years of experience in Data Engineering or related field.
• Proven ability to design and run large-scale data pipelines.
• Strong knowledge of Python, PySpark, and SQL with optimization focus.

Responsibilities

• Design, build, and maintain robust data pipelines and scalable infrastructure.
• Collaborate with Data Scientists and ML Engineers to enable data products.
• Monitor performance, troubleshoot issues, and ensure data quality and governance.

Skills

Python

PySpark

SQL

REST APIs

Data Modeling

Data Warehousing

Batch Processing

Streaming Processing

MLOps

Data Quality

Tools

Docker

Terraform

CI/CD

Git

Unix/Linux

Job description

About Coderio

Coderio designs and delivers scalable digital solutions for global companies. With a solid technical foundation and a product-oriented mindset, our teams lead complex software projects—from architecture to execution. We value autonomy, clear communication, and technical excellence. We collaborate closely with international teams and partners, building technology that creates an impact.

Learn more: Job Overview We are looking for a Senior Data Engineer based in the US to design, build, and maintain robust data pipelines and scalable infrastructure across our cloud platform. In this role, you will play a key part in enabling Data Products throughout their lifecycle—sourcing data via batch and stream processing, optimizing data architecture, and ensuring data quality. You will collaborate closely with Data Scientists and ML Engineers to automate workflows and deploy resilient data pipelines in production.

Work Location / Employment

US-based (Remote / Hybrid) — Must be authorized to work in the United States.

Cloud Platforms

AWS (Redshift, Glue, EMR, MWAA, S3) & GCP (BigQuery, Cloud Storage)

Languages & Frameworks

Python, PySpark, SQL (advanced optimization & tuning)

Data Engineering & Ops

Batch & Stream Processing, ETL/ELT Pipelines, Data Modeling, Data Warehousing, Orchestration

DevOps, Infrastructure & Tools

Docker, Terraform, CI/CD pipelines, UNIX/Linux environments, MLOps best practices

Pipeline Architecture & Development

Design, build, and deploy scalable, decoupled, and high-performance data pipelines for batch and streaming data ingestion into Data Lakes and Data Warehouses.

ETL/ELT Processing

Develop and optimize efficient data pipelines using Python, PySpark, and advanced SQL.

Collaboration with ML & Data Science

Partner with Data Scientists and ML Engineers to build resilient pipelines, automate MLOps workflows, and gather model interaction data to support retraining loops.

Performance Optimization & Monitoring

Continuously monitor, troubleshoot, and optimize pipeline execution, query performance, and storage resource utilization across cloud platforms.

Data Quality & Governance

Implement data quality frameworks and testing controls to ensure data integrity, reliability, and governance across raw and curated datasets.

DevOps & Standards

Apply MLOps and DevOps best practices, maintain CI/CD pipelines, use Docker for containerization, and document technical standards and architectures.

Experience

5+ years of experience in Data Engineering, with a proven track record of designing and running large-scale data systems.

Core Languages

Mastery of Python and advanced SQL (query optimization and performance tuning).

Cloud & Big Data Tools

Hands-on experience with AWS (Redshift, Glue, EMR, MWAA, S3) or GCP (BigQuery) and modern data processing engines (PySpark).

Engineering Ecosystem

Proficiency with UNIX environments, Docker, Git, and CI/CD pipelines.

Data & ML Lifecycle

Solid understanding of data structures, batch/streaming processing, and familiarity with MLOps or the ML deployment lifecycle.

Excellent verbal and written English communication skills.

Experience building and maintaining scalable REST APIs.

Exposure to prompt engineering workflows or generative AI data pipelines.

We are a remote-first company, passionate about technology, collaborative work, and fair compensation.