Build Scalable Cloud Data Pipelines

19 hours ago

Tirupati, Andhra Pradesh, India beBeeDataEngineer Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Title: Cloud Data Engineer

We are seeking a skilled and motivated data engineer with hands-on experience in building scalable data pipelines and cloud-native data solutions.

  • Data Ingestion Pipeline Design and Development: Design, develop, and optimize robust data ingestion pipelines using GCP services like Pub/Sub, Dataflow, and Cloud Storage.
  • BigQuery Data Warehousing: Architect and manage scalable BigQuery data warehouses to support analytics, reporting, and business intelligence needs.
  • AI/ML Workflow Support: Collaborate with data scientists and ML engineers to support AI/ML workflows using Vertex AI, including model training and deployment.
  • Quality, Reliability, and Performance: Ensure data quality, reliability, and performance across all pipeline components.
  • Cross-Functional Team 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

  • Data Engineering 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 and ETL/ELT: Solid understanding of data modeling, ETL/ELT processes, and performance optimization.
  • Programming Languages: Experience with Python, SQL, and cloud-native development practices.
  • Version Control: Familiarity with CI/CD pipelines and version control (e.g., Git).
  • Educational Background: Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

Secondary Skills (Interview-Ready Knowledge)

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

Key Responsibilities:

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