Offer summary

Qualifications:

Professional Cloud Architect Certification, Professional Data Engineer Certification, Professional Cloud Developer Certification, Professional Cloud Network Engineer Certification, Professional Cloud Security Engineer Certification, Professional Collaboration Engineer Certification, Professional Cloud DevOps Engineer, Google Cloud Certified Fellow: Hybrid Multi-Cloud, Professional Machine Learning Engineer.

Key responsibilities:

Querying, data loading, transformation and modeling in BigQuery.

Optimizing performance and integrating with other GCP services.

Monitoring, troubleshooting, security implementation, and documentation.

Continuous learning to enhance data processing pipelines.

Job description

This is a remote position.

Comerit is searching for Google Cloud Certified Professionals!

As the market is growing in the Google Cloud space, so are the number of projects for Comerit that are Google Cloud Platform based.

Specifically as we specialize in Data Transformation and Analytics, we would also prefer consultants with (GCP) BigQuery experience.

Although your GCP BigQuery experience may vary depending on the specific role and responsibilities assigned. However, here are some aspects of your BigQuery experience that may be relevant:

Querying and Analysis: Writing SQL-like queries to extract insights from large datasets stored in BigQuery. This involves understanding the data schema, performing complex aggregations, joins, and transformations, and optimizing queries for performance.
Data Loading and Extraction: Importing data from various sources into BigQuery, including files from Cloud Storage, streaming data from Pub/Sub, or data transfer from external systems. Similarly, exporting data from BigQuery to external destinations.
Data Transformation: Performing data preprocessing and transformation tasks using BigQuery's powerful SQL capabilities. This may involve data cleaning, normalization, denormalization, and creating derived tables or views.
Data Modeling and Schema Design: Designing and maintaining logical data models and schemas within BigQuery. This includes defining tables, partitions, and clustering strategies to optimize data storage and query performance.
Performance Optimization: Analyzing query execution plans, identifying bottlenecks, and optimizing query performance through techniques like partition pruning, caching, and query tuning.
Integration and Automation: Collaborating with other GCP services, such as Cloud Storage, Pub/Sub, Dataflow, or Data Studio, to build end-to-end data pipelines or integrate BigQuery with other systems. Automating job execution using scheduling tools or workflow frameworks like Cloud Composer or Cloud Data Fusion.
Monitoring and Troubleshooting: Monitoring the performance and health of BigQuery jobs, identifying and resolving issues related to data ingestion, query performance, or data consistency. Utilizing monitoring tools, logging, and error handling mechanisms.
Security and Compliance: Implementing appropriate security measures to protect sensitive data within BigQuery, adhering to data governance policies and compliance regulations. Managing access controls, data encryption, and auditing.
Documentation and Collaboration: Maintaining documentation, including data dictionaries, process flows, and technical specifications. Collaborating with cross-functional teams, including data engineers, data scientists, and business stakeholders, to understand requirements and deliver solutions.
Continuous Improvement: Staying up-to-date with the latest features and enhancements in BigQuery and related GCP services. Actively exploring opportunities to optimize data processing pipelines, enhance performance, and reduce costs.