Vrinda International
DATA ENGINEER (GCP)
Chennai, INOn-siteContractFull-time
Posted Sep 23, 2026
Data Engineer (GCP)
Summary
We are seeking an experienced Data Engineer with 6 to 9 years of expertise to join our team in Chennai on a fully onsite basis. In this critical role, you will design, build, and maintain scalable data pipelines leveraging Google Cloud Platform (GCP) to drive our data infrastructure forward. Your work will be essential in transforming raw data into actionable insights, ensuring high availability and performance for our analytics ecosystem while adhering to strict budget and operational constraints.
Responsibilities
- Design and implement robust ETL/ELT pipelines using GCP services such as BigQuery, Dataflow, and Cloud Storage.
- Develop and optimize complex SQL queries and Python scripts to process large-scale datasets efficiently.
- Collaborate with cross-functional teams to define data requirements and deliver solutions that meet business objectives.
- Monitor data pipeline performance and troubleshoot issues to ensure data integrity and system reliability.
- Participate in the F2F drive interview process on September 26 to help identify top-tier talent for the team.
Requirements
Requirements:- Bachelor's degree in Computer Science, Engineering, or a related field with 6 to 9 years of relevant experience.
- Proven expertise in Google Cloud Platform (GCP) is mandatory, with a strong portfolio of cloud-based data projects.
- Proficiency in Python and SQL is required for developing data solutions and querying databases.
- Experience working in an onsite environment with a 4-day work week in Chennai is necessary.
- Ability to work within a compensation budget of up to 30 LPA and adapt to a 30-day notice period.
Role snapshot
Job type
Full-time
Required skills
Google Cloud Platform (GCP)BigQueryDataflowCloud Storage (Google Cloud Storage)ETL/ELT pipeline design and implementationSQL (complex query development and optimization)Python for data processing and scriptingData pipeline monitoring and troubleshootingScalability and performance optimization for large-scale datasetsEnsuring high availability and system reliabilityCross-functional collaboration and data requirements gatheringTechnical interviewing and candidate assessment
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