Chief Data Pipeline Architect

21 hours ago

Kollam, Kerala, India beBeeDataEngineer Full time ₹ 15,00,000 - ₹ 18,00,000
Job Title

A Data Engineer is sought after to design and develop scalable data pipelines on Google Cloud Platform. The ideal candidate will have hands-on experience in GCP services such as Pub/Sub, Dataflow, Cloud Storage, and BigQuery.

The role involves creating robust data ingestion pipelines, architecting and managing scalable BigQuery data warehouses, collaborating with data scientists and ML engineers to support AI/ML workflows using Vertex AI, and ensuring data quality, reliability, and performance across all pipeline components.

Key Responsibilities:

  • Design, develop, and optimize data ingestion pipelines using GCP services like 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.

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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