Overview
On Site
$65 - $67
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
Skills
gcp
bigquery
etl
dataflow
Job Details
Location: Iselin NJ or Dallas, TX (Onsite)
A lead data engineer on Google Cloud Platform (Google Cloud Platform) for financial services should design, enable, and secure a modern data lake that supports advanced AI/ML workloads and regulatory reporting. This role requires expertise in scalable architecture, deep data governance, and the ability to operationalize data for analytics and compliance.
Key Responsibilities:
- Architect and build high-performance, compliant data pipeline frameworks that support batch, streaming, and real-time use cases on a well-governed financial data lake using Google Cloud Platform services such as Cloud Storage, BigQuery, Dataplex, and Data Catalog.
- Define and maintain data modeling and metadata standards to ensure traceability and auditability.
- Implement data lineage, cataloging, and observability frameworks aligned with financial data governance mandates.
- Build robust ETL/ELT pipelines in BigQuery, Dataflow, and Dataproc, with orchestration through Airflow or Cloud Composer.
- Enable real-time analytics and event-driven data integration using Pub/Sub, Kafka, or similar technologies.
- Implement data quality validation and anomaly detection frameworks.
- Develop and optimize BigQuery schemas, partitioning, and clustering strategies to manage large-scale financial datalakes and warehouses.
- Implement automated data lineage and auditability for regulatory transparency with services like Dataplex and Data Fusion Lineage.
- Operationalize AI/ML by supporting real-time and batch data pipelines, leveraging BigQuery ML, Dataflow, and Vertex AI for advanced analytics, risk scoring, and fraud detection.
Qualifications
- Bachelor s or master s degree in computer science, Information Systems, or a related field.
- 8+ years of data engineering experience (at least 2 years in financial services or regulated environments).
- Expert knowledge of BigQuery, Cloud Storage, Dataflow, and Pub/Sub on Google Cloud Platform.
- Strong experience with SQL, Spark, Python, PySpark and data orchestration tools (Airflow, Cloud Composer, Dagster).
- Demonstrated success designing modular, reusable data frameworks and libraries.
- Deep understanding of data lake and warehousing concepts, ELT/ETL patterns, and financial data modeling.
- Familiarity with security standards and compliance frameworks (e.g., PCI DSS, GDPR, SOC 2, ISO 27001).
- Experience with real-time financial data streams (trading, payments, or fraud detection).
- Familiarity with risk and regulatory reporting systems (Basel II/III, MiFID II, CCAR, IFRS).
- Exposure to data science enablement, ML Ops/Data Ops. Example: building feature pipelines for credit risk, fraud, or customer analytics.
- Experience with dbt, Looker, or Looker Studio for semantic modeling and reporting
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