
Mastering Cloud-Native Data Solutions
7 days ago
We are seeking a skilled and motivated Cloud Data Engineer with hands-on experience in building scalable data pipelines and cloud-native data solutions.
- The ideal candidate will be proficient in GCP services like Pub/Sub, Dataflow, Cloud Storage, and BigQuery, with a foundational understanding of AI/ML workflows using Vertex AI.
Key Responsibilities
- Design, develop, and optimize robust data ingestion pipelines using GCP services such as Pub/Sub, Dataflow, and Cloud Storage.
- Architect and manage scalable BigQuery data warehouses to support analytics, reporting, and business intelligence needs.
- Collaborate with data scientists and ML engineers to support AI/ML workflows using Vertex AI (AO Vertex), including model training and deployment.
- Ensure data quality, reliability, and performance across all pipeline components.
- Work closely with cross-functional teams to understand data requirements and deliver efficient solutions.
- Maintain documentation and contribute to best practices in cloud data engineering.
- A minimum of 3–6 years of experience in data engineering, with strong exposure to GCP.
- Proficiency in GCP services: Pub/Sub, Dataflow, Cloud Storage, and BigQuery.
- Solid understanding of data modeling, ETL/ELT processes, and performance optimization.
- Experience with Python, SQL, and cloud-native development practices.
- Familiarity with CI/CD pipelines and version control (e.g., Git).
- Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.
- Basic understanding of AI/ML workflows and tools within Vertex AI.
- Ability to discuss model lifecycle, deployment strategies, and integration with data pipelines.
- Awareness of MLOps principles and cloud-based ML orchestration.
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