GCP Architect

Role : Lead Data Engineer Google Cloud Platform Location : Iselin, NJ / Dallas, TX ( 100% Onsite ) Job Description We are seeking a highly skilled Lead Data Engineer (Google Cloud Platform) to architect, design, and operationalize a modern, secure financial data lake supporting advanced analytics, AI / ML workloads, and regulatory reporting. The ideal candidate will have deep experience in Google Cloud Platform , extensive knowledge of financial data governance , and a proven ability to lead data engineering initiatives in regulated environments. Key Responsibilities Architect and develop scalable, high-performance data pipeline frameworks supporting batch, streaming, and real-time workloads using Google Cloud Platform services (Cloud Storage, BigQuery, Dataplex, Data Catalog). Define and enforce data modeling, metadata, and governance standards ensuring full traceability, lineage, and auditability. Implement robust data lineage, cataloging, and observability solutions aligned with financial compliance requirements. Build enterprise-grade ETL / ELT pipelines using BigQuery, Dataflow, Dataproc, and orchestrate workflows via Airflow or Cloud Composer. Enable event-driven and real-time analytics through Pub / Sub, Kafka, or similar technologies. Develop and integrate data quality validation, anomaly detection, and automated reconciliation checks. Optimize BigQuery schema design, partitioning, clustering, and cost management for large-scale financial data lakes / warehouses. Implement automated auditability and lineage frameworks using Dataplex, Data Catalog, and Data Fusion Lineage. Support AI / ML operationalization by enabling real-time and batch feature pipelines using BigQuery ML, Dataflow, and Vertex AI for risk scoring, fraud detection, and predictive modeling. Qualifications Bachelor's or Master's degree in Computer Science, Information Systems, or related field. 8+ years of data engineering experience , with at least 2+ years in financial services or regulated industries. Expert-level experience with Google Cloud Platform services including BigQuery, Cloud Storage, Dataflow, and Pub / Sub. Strong proficiency in SQL, Spark, Python, PySpark , and data orchestration platforms (Airflow, Cloud Composer, Dagster). Proven experience designing modular, reusable data engineering frameworks and libraries. Deep understanding of data lake architectures , ELT / ETL patterns , and financial data modeling concepts. Familiarity with compliance and security frameworks : PCI DSS, GDPR, SOC 2, ISO 27001 , etc. Hands-on experience with real-time financial data streams such as trading, payments, or fraud detection. Exposure to regulatory / risk reporting systems (Basel II / III, MiFID II, CCAR, IFRS). Experience supporting DataOps / ML Ops initiatives such as building feature pipelines for credit risk, fraud, or customer analytics. Working knowledge of dbt , Looker , or Looker Studio for semantic modeling, transformations, and reporting.