Senior Data Engineer - Machine Learning

As a Senior Data Engineer with Machine Learning expertise, your role will involve designing, building, and operating reliable, scalable, and cost-efficient data pipelines. You will primarily focus on developing production-grade data systems using Python and modern orchestration tools, collaborating closely with Data Science and analytics teams to ensure high-quality, well-governed, and discoverable data across the organization. Key Responsibilities: - Data Pipeline Engineering: - Design, build, and maintain robust, scalable data pipelines using Python - Develop and manage orchestration workflows using Apache Airflow / Google Cloud Composer - Ensure high availability, fault tolerance, and performance of data workflows - Implement monitoring, alerting, and observability best practices - Production Operations & Reliability: - Own scheduling, monitoring, and incident management for data pipelines - Ensure SLAs and data freshness requirements are met - Implement proactive quality checks and validation frameworks - Optimize pipelines for performance and cost-efficiency in cloud environments - Data Governance & Collaboration: - Define and enforce data contracts across producers and consumers - Contribute to and maintain a data catalog - Partner closely with Data Science and Analytics teams for schema alignment, data discovery, and pipeline optimization - Support reproducibility and transparency of datasets used in modeling - Cloud & Architecture: - Work within Google Cloud Platform (GCP) environments - Design cost-aware architectures and storage strategies - Improve CI/CD, version control, and deployment workflows for data systems Required Skills & Experience: - Strong proficiency in Python with ML Deployment - Hands-on experience with Apache Airflow and/or Google Cloud Composer - Experience building and maintaining production data pipelines - Strong understanding of: - Workflow orchestration (Airflow) - Monitoring & alerting - Data quality frameworks - Scheduling & dependency management - Experience with cloud platforms (preferably GCP) - Solid understanding of data modeling and pipeline design principles - Experience implementing or working with data contracts - Exposure to data catalogs and metadata management - Strong operational mindset with focus on reliability and maintainability No additional details of the company are mentioned in the job description. As a Senior Data Engineer with Machine Learning expertise, your role will involve designing, building, and operating reliable, scalable, and cost-efficient data pipelines. You will primarily focus on developing production-grade data systems using Python and modern orchestration tools, collaborating closely with Data Science and analytics teams to ensure high-quality, well-governed, and discoverable data across the organization. Key Responsibilities: - Data Pipeline Engineering: - Design, build, and maintain robust, scalable data pipelines using Python - Develop and manage orchestration workflows using Apache Airflow / Google Cloud Composer - Ensure high availability, fault tolerance, and performance of data workflows - Implement monitoring, alerting, and observability best practices - Production Operations & Reliability: - Own scheduling, monitoring, and incident management for data pipelines - Ensure SLAs and data freshness requirements are met - Implement proactive quality checks and validation frameworks - Optimize pipelines for performance and cost-efficiency in cloud environments - Data Governance & Collaboration: - Define and enforce data contracts across producers and consumers - Contribute to and maintain a data catalog - Partner closely with Data Science and Analytics teams for schema alignment, data discovery, and pipeline optimization - Support reproducibility and transparency of datasets used in modeling - Cloud & Architecture: - Work within Google Cloud Platform (GCP) environments - Design cost-aware architectures and storage strategies - Improve CI/CD, version control, and deployment workflows for data systems Required Skills & Experience: - Strong proficiency in Python with ML Deployment - Hands-on experience with Apache Airflow and/or Google Cloud Composer - Experience building and maintaining production data pipelines - Strong understanding of: - Workflow orchestration (Airflow) - Monitoring & alerting - Data quality frameworks - Scheduling & dependency management - Experience with cloud platforms (preferably GCP) - Solid understanding of data modeling and pipeline design principles - Experience implementing or working with data contracts - Exposure to data catalogs and metadata management - Strong operational mindset with focus on reliability and maintainability No additional details of the company are mentioned in the job description.