GCP Data Engineer

Experience: 6 to 9 Years

Location: Phoenix, AZ, USA (Onsite)

Job Description: We are seeking an experienced GCP Data Engineer to design, build, and optimize enterprise-grade data platforms on Google Cloud. In this role, you will be responsible for building robust batch and real-time data pipelines, managing cloud data warehouses, and enabling analytics and reporting across large-scale data environments.

Key Responsibilities: Cloud Pipeline Architecture: Design, develop, and maintain automated batch and streaming data pipelines using GCP services including BigQuery, Cloud Dataflow (Apache Beam), Cloud Composer (Apache Airflow), Pub/Sub, and Cloud Storage (GCS).Data Warehousing & Optimization: Build and optimize large-scale data models in BigQuery utilizing partitioning, clustering, and materialized views to maximize query performance and control compute costs. Programming & Automation: Write production-grade Python and PySpark scripts for complex ETL/ELT transformations, data manipulation, and pipeline orchestration. CI/CD & DevOps Integration: Implement and support Git-based version control, continuous integration, and continuous delivery (CI/CD) pipelines using tools like Jenkins for code deployment, testing, and automated monitoring. BI & Reporting Support: Collaborate with business intelligence and analytics teams by preparing clean, structured datasets and assisting with data integration for reporting tools such as Tableau or Looker. AI/ML Alignment: Partner with cross-functional teams to support and integrate data models designed for downstream AI/ML solutions and predictive analytics.

Required Qualifications: Core Cloud Stack: 6 to 9 years of hands-on experience in data engineering, with strong expertise in core GCP services: BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS.Languages & Frameworks: Expert-level proficiency in Python, SQL, and PySpark / Apache Beam. DevOps Best Practices: Experience with Git, Jenkins automation, code integration, and peer code reviews within an Agile environment. Visualization & BI: Experience supporting data integration, data modeling, and performance tuning for downstream dashboards (e.g., Tableau).AI/ML Awareness: Basic understanding of AI/ML concepts and practical exposure to integrating data for AI/ML models or solutions is preferred.