Senior GCP Data Engineer - 8 To 12 Years

Crescendo Global Leadership Hiring India; Pune District, Gurugram District, Bengaluru; Hybrid; INR 2,000,000 - 3,500,000; Full time

Job summary

Crescendo Global Leadership Hiring India seeks a Senior Data Engineer to lead the migration of SAS-based data platforms to Google Cloud Platform using DBT. You will design, develop, and optimize cloud-native data pipelines, ensuring data quality, scalability, security, and compliance.

You will bring strong hands-on experience with GCP, BigQuery, and DBT, and a track record of large-scale data migration programs.

Qualifications

• Minimum 8 years of experience in Data Engineering with cloud focus.
• Proven experience migrating SAS-based platforms to cloud.
• Strong hands-on DBT, BigQuery and GCP skills required.

Responsibilities

• Migrate SAS-based data platforms to GCP using DBT.
• Design and optimize cloud-native data pipelines.
• Ensure data quality, security, and compliance across workloads.
• Document mappings, models, and data reconciliation checks.

Skills

ETL design

Data modeling

Data quality

Security & compliance

Education

Bachelor's or Master's in CS/IT/Engineering

Tools

GCP

BigQuery

DBT

Job description

We are seeking a highly skilled Senior Data Engineer to support the migration of SAS-based data platforms, ETL processes, reporting datasets, and analytical workloads to Google Cloud Platform (GCP) using DBT. The role involves designing, developing, optimizing, and supporting cloud-native data pipelines while ensuring data quality, scalability, security, and compliance.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
• 8-12 years of experience in Data Engineering.
• Minimum 3+ years of experience on GCP and BigQuery.
• Minimum 2+ years of hands‑on experience with DBT.
• Experience in large-scale data migration or modernization programs.

Key Deliverables

• SAS-to-DBT conversion specifications.
• DBT transformation models.
• BigQuery data pipelines and tables.
• Source-to-target mappings.
• Data reconciliation reports.
• Data quality validation frameworks.
• Technical and operational documentation.