Google Cloud Platform Data Engineer

Role: Senior Google Cloud Platform Data Engineer (ML & Fraud Analytics Data Platform)

Location: Austin TX (100% Onsite)

Duration: Long term Project

Cloud Platform: Google Cloud Platform

Skills Required: Google Cloud Platform, BigQuery, Python, Dataflow, Composer/Airflow, Google Cloud Storage (GCS)

Key Responsibilities & Skills

·        Design, develop, and maintain scalable ETL/data pipelines on Google Cloud Platform 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 Google Cloud Platform 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.