Senior Data Engineer

Senior Data Engineer Role Summary

Join the engineering team helping to drive data innovation across Hearst's global portfolio of consumer media businesses.

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We are seeking a highly motivated and skilled Senior Data Engineer to lead the architecture, deployment, and operation of our next-generation, data-driven platforms.

You will be instrumental in bridging the gap between Data Science and Operations, ensuring our machine learning models and core services are deployed reliably, scalably, and securely across the cloud.

This is a high-impact role requiring deep expertise in automation, cloud infrastructure, and the full Machine Learning lifecycle in a production environment.

Key Responsibilities


* MLOps Pipeline Ownership: Design, implement, and manage end-to-end MLOps pipelines for the continuous training, deployment, monitoring, and versioning of high-impact ML models (e.g., recommendation systems, content intelligence).


* Data Pipeline Design & Orchestration: Design, build, and maintain robust data ingestion and transformation pipelines, leveraging modern orchestration tools (such as Dagster or Airflow) to ensure reliable data flow for ML models.


* Kubernetes and Cloud Architecture: Architect, deploy, and maintain highly scalable, fault-tolerant infrastructure using Kubernetes (GKE) across Google Cloud Platform (GCP).


* DevOps and Automation: Drive DevOps culture and best practices, focusing on Infrastructure as Code (IaC) using Terraform or similar tools, and establishing robust CI/CD workflows.


* CI/CD Implementation: Configure and manage automated deployment and testing pipelines using GitHub Actions and other integrated tools, ensuring fast and reliable code delivery.


* Core Development: Write clean, efficient, and well-tested code in Python for automation scripts, infrastructure tooling, and glue services connecting cloud components.


* API and Service Deployment: Design, develop, and deploy robust, high-performance Python APIs (using frameworks like FastAPI or Flask) to serve machine learning predictions and core application logic in a production environment.


* Monitoring and Observability: Implement comprehensive monitoring, logging, and alerting solutions (e.g., Prometheus, Grafana, Cloud Logging) to maintain high availability and quickly diagnose production issues.


* Security and Compliance: Ensure platform integrity by implementing security best practices, access controls, and adhering to media industry compliance standards.


* Collaboration and Mentorship: Work closely with data scientists, software engineers, and product managers; provide technical guidance and mentorship to junior team members.

Required Qualifications


* 5+ years of professional experience in DevOps, Cloud Engineering, or a related field, with at least 2 years specifically focusing on MLOps in a production environment.


* Deep expertise in Python for development, scripting, and automation.


* Proven...