Experience: 10+ Years
Cloud Platform: GCP
Skills Required: GCP, BigQuery, Python, Dataflow, Composer/Airflow, Google Cloud Storage (GCS)
Key Responsibilities & Skills
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 modeling, 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.

GCP Data Engineer

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