IAM GCP Data Engineer
Job Summary
Job Description
This role focuses on designing, building, and maintaining data pipelines using GCP services such as BigQuery, Dataflow, Dataproc, and Cloud Datastore. The responsibilities also include implementing solutions for ingesting, transforming, and storing data from various sources. A key aspect of this position is configuring and managing IAM roles and permissions to ensure data security and compliance. The engineer will also implement granular access control policies, collaborate with data governance teams, apply security best practices, and monitor data pipelines for quality and reliability.
Must have:
- Strong understanding of data processing principles
- Proficiency in GCP services (BigQuery, Dataflow, Dataproc, Cloud Storage)
- Experience with IAM roles and access control policies on GCP
- Familiarity with Python, SQL
- Understanding of data security principles
3 skills required
Job Details
IAM GCP Data Engineer
Key Responsibilities of a GCP Data Engineer with IAM Experience:
- Data Engineering: Strong understanding of data processing principles, data pipelines, and data warehousing concepts.
- GCP Services: Proficiency in using GCP services for data engineering, including BigQuery, Dataflow, Dataproc, and Cloud Storage.
- IAM: Experience with IAM roles, permissions, and access control policies on GCP.
- Programming Languages: Familiarity with languages like Python, SQL
- Security: Understanding of data security principles, encryption, and access control.
- Data Governance: Knowledge of data governance principles and best practices.
Data Processing Systems: Design, build, and maintain data pipelines using GCP services like BigQuery, Dataflow, Dataproc, and Cloud Datastore.
Data Ingestion and Transformation: Implement solutions for ingesting, transforming, and storing data from various sources, including structured, semi-structured, and unstructured formats.
IAM Implementation: Configure and manage IAM roles and permissions to grant appropriate access to data resources, ensuring data security and compliance.
Access Control: Implement granular access control policies based on user roles, resource types, and other attributes.
Data Governance: Collaborate with data governance teams to establish and enforce data policies and standards.
Security Best Practices: Apply security best practices to protect data at rest and in transit, including encryption, access controls, and auditing.
Data Observability: Monitor and troubleshoot data pipelines, identifying and resolving issues to ensure data quality and reliability.
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