GCP Data Engineer with AI/ML Integration & MLOps

[Upgrade to PRO to see contact] H1b Workable Relocation is Fine Role : GCP Data Engineer with AI/ML Integration & MLOps Location : Irving, Texas 75039 (100% onsite) Hire type : Contract Interview Mode : 1 Video interview and client interview will be in person from any one of the Client office In usa Implementation partner - ********** End Client - Banking Client Experience: 9+   Overview We are seeking a GCP Data Engineer with deep, hands-on architectural and development experience in Google Cloud Platform’s big data ecosystem. You will be responsible for designing, building, and optimizing a modern data lakehouse architecture. Your primary focus will be leveraging BigLake, BigQuery, Google Cloud Storage (GCS), and Vertex AI to create seamless, scalable data pipelines and machine learning integrations that drive business intelligence and predictive analytics.   Key Responsibilities Lakehouse Architecture & Development: Architect and maintain a scalable data lakehouse using Google Cloud Storage (GCS) as the foundational data lake and BigLake to unify data warehouses and data lakes. Implement fine-grained security (row-level and column-level access controls) and data governance across open file formats (Parquet, Iceberg, ORC) using BigLake. Data Warehousing & Optimization: Design and manage complex, highly scalable data models within Big Query. Perform deep performance tuning and cost optimization of Big Query jobs utilizing clustering, partitioning, materialized views, and slot capacity management.   AI/ML Integration & MLOps:  Collaborate with Data Scientists to operationalize machine learning models using Vertex AI. Build robust data pipelines to feed Vertex AI Feature Store, manage model training workflows and deploy ML models into production. Utilize Big Query ML (BQML) for in-database predictive modeling and analytics where appropriate.   Data Pipeline Engineering: Design, develop, and orchestrate batch and streaming data pipelines (using tools like Dataflow, Dataproc, or Cloud Composer/Airflow) to ingest data from diverse sources into GCS and BigQuery. Data Governance & Best Practices: Establish data lifecycle management policies in GCS. Ensure data quality, reliability, and security compliance across the entire GCP big data stack. Mentor junior engineers and lead code/architecture reviews.   Required Qualifications Experience: 5+ years of dedicated Data Engineering experience, with at least 3+ years focused exclusively on the Google Cloud Platform (GCP).   Deep GCP Big Data Expertise: BigQuery: Expert-level knowledge of BigQuery architecture, advanced SQL, analytical functions, query profiling, and optimization techniques. BigLake: Proven experience utilizing BigLake for multi-cloud or lakehouse architectures, managing open-source formats (e.g., Apache Iceberg/Parquet), and enforcing unified security policies. GCS: Deep understanding of GCS storage classes, object lifecycle management,