Senior GCP Data Engineer (ML & Fraud Analytics Data Platform)

Indents: SF_OP_173885-6 / SF_OP_173885-7

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

Location: Austin TX (100% Onsite)

1st Preference: FTE – Max Salary: $100K

2nd Preference: Contract to Hire – Max Rate: $70/hr

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.