Gcp Data Engineer

Tredence; Kolkata District, Gurugram District, Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tredence is seeking a Data Engineer in Kolkata to design, build, and maintain scalable data pipelines on Google Cloud Platform. You will transform data for analytics, write optimized SQL and Python code, and collaborate with cross-functional teams to ensure data quality and performance.

Join a team that designs cloud-native data solutions, leads end-to-end delivery, and enforces best practices across data engineering projects.

Qualifications

• Experience designing data pipelines for analytics
• Strong SQL and Python coding abilities
• Familiarity with data quality, reliability, and performance tuning

Responsibilities

• Design, build, and maintain cloud-native data pipelines on GCP
• Transform data for analytics and reporting
• Collaborate with cross-functional teams and follow deployment best practices
• Own end-to-end data solutions delivery and code reviews

Skills

SQL

Python

Data modeling

Data wrangling

Tools

BigQuery

Dataflow

Cloud Composer

dbt

Job description

Role & responsibilities

We are looking for Data Engineers to design, build, and maintain scalable, cloud-native data solutions on Google Cloud Platform. The role involves developing reliable data pipelines, transforming data for analytics, and collaborating with cross-functional teams.

Key Responsibilities

• Design, develop, and maintain data pipelines using BigQuery, Dataflow, and Cloud Composer
• Build and manage data transformations using dbt
• Write efficient and optimized SQL queries and Python code
• Ensure data quality, reliability, and performance of data pipelines
• Collaborate with stakeholders to understand data and reporting requirements
• Follow engineering standards, documentation, and deployment best practices

Additional Responsibilities

• Own end-to-end design and delivery of complex data solutions
• Translate business requirements into scalable technical architectures
• Lead code reviews and enforce data engineering best practices