GCP Data Engineer with AI/ML

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

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, and optimizing GCS for big data workloads. Vertex AI: Hands-on experience with Vertex AI pipelines, endpoints, feature stores, or deploying ML models into scalable data environments. Programming Skills: Advanced proficiency in Python and SQL. Familiarity with Java, Scala, or Go is a plus. Data Orchestration & CI/CD: Experience with orchestration tools (e.g., Apache Airflow, Cloud Composer) and modern CI/CD pipelines (e.g., GitHub Actions, Terraform, Cloud Build).Preferred/Bonus QualificationsGCP Certifications: Google Cloud Certified - Professional Data Engineer or Professional Machine Learning Engineer.