Overview
Remote
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
Skills
Bigquery
GCP
data
Job Details
BigQuery Data Engineer
Remote
Job Summary:
We are seeking a skilled and experienced Google Cloud Platform BigQuery Data Engineer to join our data engineering team. The ideal candidate will have a strong background in SQL, Google BigQuery, and Google Cloud Platform (Google Cloud Platform) services such as Cloud Composer (DAGs), Compute Engine, and App Engine. You will be responsible for designing, developing, and optimizing data pipelines and analytics solutions that support business decision-making.
Key Responsibilities:
- Design and implement scalable data pipelines using Google Cloud Platform services.
- Develop and optimize queries in BigQuery for large-scale data processing.
- Build and manage DAGs using Cloud Composer (Apache Airflow).
- Integrate data from various sources and ensure data quality and consistency.
- Collaborate with data scientists, analysts, and business stakeholders to understand data requirements.
- Monitor and troubleshoot data workflows and performance issues.
- Automate data ingestion, transformation, and loading processes.
- Ensure security and compliance of data solutions within Google Cloud Platform.
Required Skills & Qualifications:
- 5 8 years of experience in data engineering or related roles.
- Strong proficiency in SQL and experience with Google BigQuery.
- Hands-on experience with Cloud Composer (DAGs), Compute Engine, and App Engine.
- Solid understanding of data modeling, ETL/ELT processes, and data warehousing concepts.
- Experience with version control tools (e.g., Git) and CI/CD pipelines.
- Familiarity with Python or other scripting languages is a plus.
- Excellent problem-solving and communication skills.
- Google Cloud Platform certification (e.g., Professional Data Engineer) is a plus.
Preferred Qualifications:
- Experience with other Google Cloud Platform services like Cloud Storage, Pub/Sub, and Dataflow.
- Exposure to DevOps practices and infrastructure as code (Terraform, Deployment Manager)
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