Cloud Data Architect

1 day ago

Mumbai, Maharashtra, India beBeeDataEngineer Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Title: Cloud Data Engineer

We are seeking a skilled and motivated Cloud Data Engineer with hands-on experience in building scalable data pipelines and cloud-native data solutions on Google Cloud Platform.

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.

Main Responsibilities:

  • Design and Develop Data Ingestion Pipelines: Utilize GCP services such as Pub/Sub, Dataflow, and Cloud Storage to create robust data ingestion pipelines.
  • Manage Scalable Data Warehouses: Architect and manage scalable BigQuery data warehouses to support analytics, reporting, and business intelligence needs.
  • Collaborate with Data Scientists: Collaborate with data scientists and ML engineers to support AI/ML workflows using Vertex AI, including model training and deployment.
  • Maintain Data Quality: Ensure data quality, reliability, and performance across all pipeline components.
  • Work Closely with Cross-Functional Teams: Work closely with cross-functional teams to understand data requirements and deliver efficient solutions.
  • Contribute to Best Practices: Maintain documentation and contribute to best practices in cloud data engineering.

Required Skills & Qualifications:

  • 3–6 Years of Experience: 3–6 years of experience in data engineering, with strong exposure to GCP.
  • GCP Services Proficiency: Proficiency in GCP services: Pub/Sub, Dataflow, Cloud Storage, and BigQuery.
  • Data Modeling Understanding: Solid understanding of data modeling, ETL/ELT processes, and performance optimization.
  • Cloud-Native Development Skills: Experience with Python, SQL, and cloud-native development practices.
  • CI/CD Pipeline Familiarity: Familiarity with CI/CD pipelines and version control (e.g., Git).
  • Education: Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

Secondary Skills:

  • Awareness of MLOps Principles: Basic understanding of AI/ML workflows and tools within Vertex AI.
  • Model Deployment Strategies: Ability to discuss model lifecycle, deployment strategies, and integration with data pipelines.
  • Cloud-Based ML Orchestration: Awareness of MLOps principles and cloud-based ML orchestration.

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