Job
Description
As a member of the Platform Observability Engineering team within Ford's Data Platforms and Engineering (DP&E) organization, you will contribute to building and maintaining a top-tier platform for monitoring and observability. This platform focuses on the four golden signalslatency, traffic, errors, and saturationproviding essential data to support operations, root cause analysis, continuous improvement, and cost optimization. You will collaborate with platform architects to help design, develop, and maintain a scalable and reliable platform, ensuring smooth integration with systems used across various teams. Your contributions will be key in improving MTTR and MTTX through increased visibility into system performance. Working with stakeholders, you will integrate observability data into their workflows, develop insightful dashboards and reports, continuously improve platform performance and reliability, optimize costs, and stay updated with industry best practices and technologies. The role focuses on building and maintaining a robust platform rather than developing individual monitoring tools, creating a centralized, reliable source of observability data that empowers data-driven decisions and accelerates incident response across the organization. Responsibilities: - Design and Build Data Pipelines: Architect, develop, and maintain scalable data pipelines and microservices supporting real-time and batch processing on GCP. - Service-Oriented Architecture (SOA) and Microservices: Design and implement SOA and microservices-based architectures for modular, flexible, and maintainable data solutions. - Full-Stack Integration: Contribute to the seamless integration of front-end and back-end components, ensuring robust data access and UI-driven data exploration. - Data Ingestion and Integration: Lead the ingestion and integration of data from various sources into the data platform, ensuring standardized and optimized data for analytics. - GCP Data Solutions: Utilize GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms meeting business needs. - Data Governance and Security: Implement and manage data governance, access controls, and security best practices while leveraging GCP's native security features. - Performance Optimization: Continuously monitor and improve the performance, scalability, and efficiency of data pipelines and storage solutions. - Collaboration and Best Practices: Define best practices, design patterns, and frameworks for cloud data engineering by closely working with data architects, software engineers, and cross-functional teams. - Automation and Reliability: Automate data platform processes to enhance reliability, reduce manual intervention, and improve operational efficiency. Qualifications: - Technical Skills: Proficiency in Java, Angular, or any JavaScript technology with experience in designing and deploying cloud-based data pipelines and microservices using GCP tools like BigQuery, Dataflow, and Dataproc. - Service-Oriented Architecture and Microservices: Strong understanding of SOA, microservices, and their application within a cloud data platform context. Develop robust, scalable services using Java Spring Boot, Python, Angular, and GCP technologies. - Full-Stack Development: Knowledge of front-end and back-end technologies enabling collaboration on data access and visualization layers (e.g., React, Node.js). - Design and develop RESTful APIs for seamless integration across platform services. - Implement robust unit and functional tests to maintain high standards of test coverage and quality. - Database Management: Experience with relational (e.g., PostgreSQL, MySQL) and NoSQL databases, as well as columnar databases like BigQuery. - Data Governance and Security: Understanding of data governance frameworks and implementing RBAC, encryption, and data masking in cloud environments. - CI/CD and Automation: Familiarity with CI/CD pipelines, Infrastructure as Code (IaC) tools like Terraform, and automation frameworks. - Manage code changes with GitHub and troubleshoot and resolve application defects efficiently. - Ensure adherence to SDLC best practices, independently managing feature design, coding, testing, and production releases. - Problem-Solving: Strong analytical skills with the ability to troubleshoot complex data platform and microservices issues. Certifications (Preferred): GCP Data Engineer, GCP Professional Cloud,
