Senior GCP Data Engineer

GCP (Good Clinical Practices), GCP (Good Clinical Practices), Cloud Computing, Python Programming/Scripting Language, Data Management, Database Extract Transform and Load (ETL), Cloud Storage, Scalable System Development, Training Data Sets, Data Modeling, * Design, develop, and maintain scalable ETL/data pipelines on GCP using Python, Dataflow, BigQuery, Cloud Storage, Composer/Airflow, and Control-M to support fraud analytics, ML, and enterprise data initiatives.

• Build and optimize ML-ready datasets, feature engineering pipelines, and reusable data assets for model training, validation, and production deployment.
• Develop high-quality Python solutions following coding standards, security best practices, resiliency, reliability, and performance optimization principles.
• Strong expertise in SQL, BigQuery/PostgreSQL, data modelling, database concepts, and large-scale data processing.
• Implement data quality, reconciliation, lineage, metadata management, governance, and monitoring controls to ensure trusted and auditable data pipelines.
• Design and support CI/CD-enabled data engineering platforms, automated deployments, and integration with enterprise data ecosystems including Dataiku, Neo4j, REST APIs, and cloud-native services.
• Collaborate with Data Scientists and ML Engineers to support feature availability, data access, pipeline orchestration, integration testing, and ML operationalization.
• Strong analytical, problem-solving, and troubleshooting skills; exposure to GenAI use cases and MLOps ecosystems is a plus.

Preferred Experience

• Overall 10+ years of experience
• 5+ years on GCP Data Engineering
• 5+ years with Python/Dataflow-based ETL development
• 3+ years with Composer/Airflow, BigQuery/PostgreSQL, and Google Cloud Storage
• Experience supporting fraud detection, risk analytics, or ML data platforms preferred.