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# Google Cloud Platform Data Engineer

- **Company:** HMG America
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Query Performance, Airflow, BigQuery, Cloud Storage, Computer Programming, Data as a Services, Data Architecture, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Masking, Data Profiling, Data Warehousing, Data Flow Control, Identity and Access Management, Python (Programming Language), Performance Tuning, SQL Databases, Data Streaming, Data Processing, Scripting, Google Cloud, Data Storage Technologies, Data Ingestion, Sql Optimization, Star Schema, Real Time Data, Data Pipelines
- **Published:** November 12, 2026
- **Apply:** https://www.dice.com/job-detail/04041c44-3bf5-4158-a5b7-3645f147fb36

## About the Role

* 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).

## Description

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., * 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.