Google Cloud Platform Data Engineer

Position Overview

The ideal candidate will design, build, and maintain scalable, production-grade batch and streaming data pipelines using core Google Cloud Platform (Google Cloud Platform) technologies.

Key Responsibilities

• Pipeline Development: Design, build, and optimize scalable batch and real-time data ingestion pipelines using Google Cloud Platform Dataflow, Pub/Sub, and Cloud Composer (Airflow).
• Data Warehousing & Modeling: Create optimized data architectures and dimensional models in BigQuery to support enterprise analytics, reporting, and data science teams.
• Data Integration & Transformation: Develop clean, scalable Python code and complex SQL scripts/dbt models for ETL/ELT workflows.
• Cloud Storage & Security: Manage data storage strategies on Cloud Storage (GCS) and implement IAM security, data masking, and access controls.
• Performance Tuning & Optimization: Monitor pipeline execution, conduct data profiling, and optimize BigQuery query performance and resource utilization to minimize operational costs.
• Collaboration: Partner with cross-functional teams (Data Analysts, Data Scientists, and Business Stakeholders) to gather requirements and translate them into robust technical solutions.

Required Qualifications & Technical Skills

• Experience: 5+ years of hands-on data engineering experience, with at least 3+ years dedicated to Google Cloud Platform data services.
• Google Cloud Platform Stack: Deep expertise in BigQuery, Cloud Storage (GCS), Dataflow, Pub/Sub, and Cloud Composer (Apache Airflow).
• Programming & SQL: Strong proficiency in Python (for data processing/scripting) and Advanced SQL (window functions, optimization, complex joins).
• ETL/ELT & Orchestration: Proven experience building idempotent workflows and data pipelines (e.g., using dbt, Airflow, or Dataflow).
• Data Warehousing: Solid understanding of data modeling principles (Star Schema, Snowflake Schema, SCD Type I/II).