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Vrinda International

Data Engineer – Data Platforms (Google Cloud)

Vrinda International Gurgaon, Haryana, India

Human Resources Services · 2-10 employees

May 29
data-engineer Senior (5-10 yrs) Full-time India
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About the role

Design, build, and operationalize scalable batch and real-time data pipelines on Google Cloud Platform. Manage data workflows and optimize data warehouse solutions using tools like BigQuery, Dataflow, and Cloud Composer.

What they look for

Google Cloud Platform BigQuery Data Engineering Spark PySpark SQL Dataflow Dataproc Pub/Sub Airflow Cloud Composer Kafka Hive Big Data ETL Data Warehouse

Requirements

Requires 8+ years of total experience with at least 7 years of relevant experience in data engineering. Must have strong hands-on expertise in GCP, Spark, SQL, and building complex ETL pipelines.

Full description

? Hiring: Data Engineer – Data Platforms (Google Cloud)

? Location: Gurgaon

Work Mode: Work From Office (Day 1 Mandatory)

Interview Mode: Face-to-Face (Mandatory)

We are looking for an experienced Data Engineer – Google Cloud Platform (GCP) with strong expertise in building scalable data pipelines and cloud-based data solutions.

Key Responsibilities:

  • Design, build, and operationalize batch & real-time data pipelines on GCP
  • Develop data solutions using Dataflow, Dataproc, Pub/Sub, and BigQuery
  • Build and optimize data warehouse solutions and data lake architectures
  • Work with BigQuery, BigQuery ML, Cloud Storage, Cloud SQL, Spanner, and Bigtable
  • Perform Spark job optimization, debugging, and performance tuning
  • Manage data workflows using Cloud Composer (Airflow) and Cloud Scheduler
  • Build and support data ingestion pipelines (batch & streaming)
  • Work with stakeholders to deliver scalable data engineering solutions

Required Skills:

  • 8+ years total experience, 7+ years relevant experience
  • Strong hands-on experience in GCP (must have)
  • Expertise in BigQuery, BigQuery ML, SQL
  • Experience in Data Engineering, Data Warehouse, and ETL pipelines
  • Strong knowledge of Spark, PySpark, Kafka, Hive, Big Data fundamentals
  • Experience with Dataflow, Dataproc, Pub/Sub, Airflow (Composer)
  • Strong debugging, performance tuning, and optimization skills

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