Cloud Data Specialist

13 hours ago


Anantapur, Andhra Pradesh, India beBeeDataEngineer Full time ₹ 15,00,000 - ₹ 20,00,000

We're seeking a skilled and motivated Data Specialist with hands-on experience in building scalable data solutions on the cloud.

The ideal candidate will have strong technical skills, including proficiency in Python, SQL, and cloud-native development practices.

A solid understanding of data modeling, ETL/ELT processes, and performance optimization is required.

Familiarity with CI/CD pipelines and version control is also essential.

Additionally, the successful candidate will have 3-6 years of experience in data engineering, with strong exposure to GCP services.

  • Data Engineering
  • Cloud Solutions
  • Data Analysis
  • Machine Learning
Responsibilities:
  1. Design, Develop, and Optimize robust data ingestion pipelines using GCP services like Pub/Sub, Dataflow, and Cloud Storage.
  2. Architect and Manage scalable BigQuery data warehouses to support analytics, reporting, and business intelligence needs.
  3. Collaborate with data scientists and ML engineers to support AI/ML workflows using Vertex AI.
  4. Ensure Data Quality, reliability, and performance across all pipeline components.
  5. Work Closely with cross-functional teams to understand data requirements and deliver efficient solutions.
  6. Maintain Documentation and contribute to best practices in cloud data engineering.
Requirements:
  • 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:
  • Brief knowledge 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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