Senior Cloud Data Specialist

1 week ago

Bareilly, Uttar Pradesh, India beBeeDataEngineer Full time ₹ 15,00,000 - ₹ 20,00,000
Cloud Data Engineer Position

We are seeking a skilled and motivated Cloud Data Engineer to join our team. As a key member of our organization, you will be responsible for building scalable data pipelines and cloud-native data solutions on Google Cloud Platform.

This is an exciting opportunity to work with cutting-edge technology and contribute to the growth and success of our company.

Key Responsibilities
  • Data Ingestion Pipelines: Design, develop, and optimize robust data ingestion pipelines using GCP services such as Pub/Sub, Dataflow, and Cloud Storage.
  • BigQuery Data Warehouses: Architect and manage scalable BigQuery data warehouses to support analytics, reporting, and business intelligence needs.
  • AI/ML Workflows: Collaborate with data scientists and ML engineers to support AI/ML workflows using Vertex AI, including model training and deployment.
  • Data Quality and Performance: Ensure data quality, reliability, and performance across all pipeline components.
  • Cross-Functional Collaboration: Work closely with cross-functional teams to understand data requirements and deliver efficient solutions.
  • Documentation and Best Practices: Maintain documentation and contribute to best practices in cloud data engineering.
Required Skills & Qualifications
  • 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.
Secondary Skills (Interview-Ready Knowledge)
  • 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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