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Niche GCP and vector-search specialization lowers applicant competition despite metro location.
Specialized GCP, BigQuery, unstructured data, and vector-search expertise reduces cross-industry transferability.
Explicit 7-10 years and mandatory GCP, BigQuery, embeddings, and Airflow skills create high shortlisting strictness.
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Design and develop scalable data ingestion pipelines and semantic data layers for unstructured and multi-modal content on Google Cloud Platform.
Build and optimize BigQuery-based data warehouses, semantic models, and vector-based search solutions for analytics and enterprise use.
Orchestrate end-to-end workflows using GCP services like Cloud Composer or Workflows and integrate semantic platforms with AI applications.
7 to 10 years of relevant experience in data engineering or related roles.
Proficiency with Google Cloud Platform components: BigQuery, Cloud Storage, Document AI, Vertex AI.
Strong skills in SQL, data modeling, unstructured document processing, vector embeddings, and workflow orchestration (Airflow/Cloud Composer).
Location: Chennai, Tamil Nadu, India (onsite).
Experienced data engineer with a focus on GCP services for handling unstructured and multi-modal data at scale.
Skilled in building semantic data models and vector search systems integrated with enterprise analytics platforms.
Capable of managing complex workflows and collaborating across business and technical teams to define semantic data standards.