Lead Data Engineer – Google Cloud Platform

Overview

On Site
65 - 67
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
No Travel Required
Unable to Provide Sponsorship

Skills

data engineer
gcp
google cloud

Job Details

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.

 

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