Google Cloud Platform Data Engineer

*Only W2 Profiles*
Location: San Francisco Bay Area, CA (Local Candidates Only)

We are seeking an experienced Google Cloud Platform Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data processing solutions using Google Cloud Platform (Google Cloud Platform). The ideal candidate will have strong experience in data engineering, ETL/ELT development, data warehousing, and cloud-based analytics solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Google Cloud Platform.
• Build and optimize data processing workflows using BigQuery, Dataflow, and Cloud Data Fusion.
• Develop ETL/ELT pipelines to ingest, transform, and integrate data from multiple sources.
• Work with Python and SQL to perform data transformations, data validation, and processing.
• Implement data warehousing and data modeling solutions using BigQuery.
• Orchestrate and schedule data pipelines using Cloud Composer (Apache Airflow) or similar tools.
• Monitor pipeline performance, troubleshoot failures, and optimize processing costs.
• Implement data quality, security, governance, and access control best practices.
• Collaborate with data analysts, architects, developers, and business stakeholders.
• Maintain technical documentation and follow data engineering best practices.

Required Skills

• Strong hands-on experience with Google Cloud Platform (Google Cloud Platform).
• Experience with BigQuery, Dataflow, and Cloud Storage (GCS).
• Strong SQL and Python programming skills.
• Experience building and maintaining ETL/ELT data pipelines.
• Knowledge of data warehousing, data modeling, and database concepts.
• Experience with workflow orchestration tools such as Cloud Composer or Apache Airflow.
• Understanding of cloud security, IAM, monitoring, and performance optimization.
• Excellent communication and problem-solving skills.

Preferred Qualifications

• Experience with Pub/Sub, Dataproc, Dataform, or dbt.
• Knowledge of Apache Spark, PySpark, and distributed data processing.
• Experience with CI/CD, Git, Terraform, or infrastructure as code.
• Familiarity with streaming and batch data processing architectures.