Senior Data Engineer - USA
科迪奥为全球公司设计和提供可扩展的数字解决方案。凭借坚实的技术基础和产品导向思维，我们的团队领导复杂软件项目从建筑到执行。我们重视自主化，清晰沟通和技术卓越性。我们与国际团队和合作伙伴密切合作，建立产生影响力的技术。更多信息：http://coderio.com 为什么加入科迪奥？在科迪奥，我们不管地点如何都珍惜人才。我们是一个远程第一公司，热衷于技术，协作工作和公平补偿。我们提供了一个具有挑战性的环境，有真正的机会增长。如果你有兴趣构建具有影响力的解决方案，我们正在寻找你。现在申请。工作概述 我们在美国寻求一位高级数据工程师来设计，建造和维护我们的云平台中强大的数据管道和可扩展的基础设施。在这个角色上，你将发挥重要作用，使数据产品在其整个生命周期中通过批量处理和流程处理获取数据，优化数据架构并确保数据质量。你将与数据科学家和 ML 工程师密切合作，自动化工作流程并部署生产中的弹性数据管道。· 工作地点 / 就业：基于美国 （远程/混合）必须获得在美国工作的授权。技术堆和关键技术 ·云平台：AWS (Redshift,Glue,EMR,MWAA,S3) & GCP （BigQuery,云存储）·语言与框架：Python,Pyspark,SQL （高级优化和调整）·数据工程&运营：批量和流处理，ETL/ELT 管道，数据建模，数据仓库，配套 · DevOps,基础设施和工具：Docker,最佳形式，CI/CD 管道，UNIX/Linux 环境，MLOPs 实践 ·管道建筑和开发：· ETL/ELT 处理：使用 Python, PySpark 和先进 SQL 开发和优化高效的数据管道。·与 ML&Data Science 合作：与数据科学家和 ML 工程师合作建立弹性管道，自动化 MLOps 工作流程，并收集模型互动数据以支持重训循环。·性能优化和监测：持续监控，解决故障和优化管道执行。
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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: http://coderio.com
Why join Coderio?
At Coderio, we value talent regardless of location. We are a remote-first company, passionate about technology, collaborative work, and fair compensation. We offer an inclusive, challenging environment with real opportunities for growth. If you are motivated to build impactful solutions, we’re looking for you.
Apply now.
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.
Tech Stack & Key Technologies
- 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
Key Responsibilities
- 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.
Qualifications & Requirements
- 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.
- Communication: Excellent verbal and written English communication skills.
Nice to Have
- Experience with Infrastructure as Code (e.g., Terraform).
- Experience building and maintaining scalable REST APIs.
- Exposure to prompt engineering workflows or generative AI data pipelines.