GCP BigQuery Developer / Data Engineer (2 To 5 Years)

Infosys; Dadri, Hyderabad, Bengaluru; Hybrid; INR 700,000 - 1,300,000; Full time

Job summary

Infosys is seeking a GCP BigQuery Developer / Data Engineer with 2-5 years of experience to design, develop, and maintain cloud-based data solutions on Google Cloud Platform (GCP). The role requires expertise in BigQuery, SQL, Python, ETL/ELT development, and cloud data engineering to build scalable data pipelines.

You will design and optimize data solutions, build ETL/ELT pipelines on GCP, and collaborate with analysts and data scientists to meet data requirements, while following data

Qualifications

• 2-5 years of hands-on data engineering on cloud platforms.
• Experience with Google Cloud Platform services and data pipelines.
• Strong SQL and Python scripting for data processing.

Responsibilities

• Design, develop, and optimize data solutions using Google BigQuery.
• Build and maintain scalable ETL/ELT pipelines on GCP.
• Develop data ingestion and processing workflows from multiple sources.
• Create and manage data warehouses and data lakes on Google Cloud.
• Optimize queries for performance, cost, and scalability.
• Collaborate with stakeholders to understand data requirements.
• Implement data quality checks and monitoring.

Skills

GCP

BigQuery

SQL

Python

ETL/ELT

Job description

Position: GCP BigQuery Developer / Data Engineer
Experience: 2-5 Years
Location: Pan India
Employment Type: Full-Time

Job Summary

We are seeking a skilled GCP BigQuery Developer / Data Engineer with 2-5 years of experience in designing, developing, and maintaining cloud-based data solutions on Google Cloud Platform (GCP). The ideal candidate should have expertise in BigQuery, SQL, Python, ETL/ELT development, and cloud data engineering to build scalable and high-performance data pipelines.

Key Responsibilities

• Design, develop, and optimize data solutions using Google BigQuery.
• Build and maintain scalable ETL/ELT pipelines on GCP.
• Develop data ingestion, transformation, and processing workflows from multiple data sources.
• Create and manage data warehouses and data lakes on Google Cloud.
• Optimize BigQuery queries for performance, scalability, and cost efficiency.
• Work closely with business stakeholders, analysts, and data scientists to understand data requirements.
• Implement data quality checks, data validation, and monitoring frameworks.
• Develop reusable datasets, views, stored procedures, and reporting layers.
• Troubleshoot and resolve data pipeline and production-related issues.
• Follow data security, governance, and compliance standards.