KANINI is seeking a highly skilled Data Engineer with deep expertise in Google Cloud Platform (GCP) and modern data architecture. The ideal candidate will have hands-on experience designing scalable data pipelines, implementing Medallion Architecture, and building robust enterprise-grade data solutions. This role requires strong technical proficiency in BigQuery, PySpark, Dataflow, and Airflow, along with a solid understanding of cloud data governance, performance optimization, and CI/CD practices.

Key Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines on GCP
• Implement and manage Medallion Architecture (Bronze, Silver, Gold layers) for data processing
• Build high-performance data transformations using Python and PySpark
• Develop and optimize complex SQL queries for analytical workloads
• Work extensively with BigQuery for large-scale data processing and performance tuning
• Develop and deploy pipelines using Cloud Dataflow
• Orchestrate workflows using Cloud Composer (Apache Airflow)
• Manage data storage and lifecycle using Google Cloud Storage (GCS)
• Implement version control and CI/CD pipelines using Git-based tools
• Ensure data security, governance, and access control using GCP IAM
• Optimize data solutions for performance, scalability, reliability, and cost-efficiency

Required Skills & Experience

• Strong hands-on experience with Google Cloud Platform (GCP)
• Expertise in BigQuery (partitioning, clustering, query optimization)
• Proven experience implementing Medallion Data Architecture
• Strong programming skills in Python and PySpark
• Hands-on exposure on Java
• Advanced proficiency in SQL (complex joins, window functions, performance tuning)
• Hands-on experience with Cloud Dataflow
• Experience with Cloud Composer (Airflow) for orchestration
• Experience working with Google Cloud Storage (GCS)
• Knowledge of version control systems (Git) and CI/CD practices
• Strong understanding of GCP IAM and cloud security best practices

Preferred Qualifications

• Experience working with large-scale enterprise data platforms
• Knowledge of data warehousing and data lake concepts
• Familiarity with real-time streaming frameworks
• Experience in data governance and data quality frameworks
• Exposure to Agile/Scrum methodologies