Browse categories
Explore
Fiverr Pro
English
$
USD
I will develop gcp data pipelines using bigquery, dataflow and cloud storage
Data Engineering Expert and Cloud Solutions Architect
Experienced Azure data engineer with 13+ years building scalable data solutions using Microsoft Fabric, Azure Data Factory (ADF), Azure Data Lake, and Synapse Analytics. I also work across Snowflake, ...
About this Gig
Leverage Google's world-class data infrastructure to build lightning-fast analytics pipelines that can query petabytes of data in seconds.
Need enterprise-scale analytics with Google's legendary performance and ML capabilities? Want serverless, fully-managed platforms eliminating infrastructure headaches? I'm a Google Cloud Certified Professional building solutions using the same technology powering Google Search and YouTube.
What You'll Receive:
- BigQuery data warehouse optimized for massive-scale analytics
- Cloud Dataflow pipelines for real-time and batch processing
- Cloud Storage with multi-regional redundancy and lifecycle management
- AI/ML integration ready for advanced analytics and predictive modeling
- Cost-optimized queries reducing BigQuery costs by 90%+
- Global-scale architecture with consistent worldwide performance
My GCP Expertise:
Google Cloud Certified Data Engineer with 13+ years GCP experience, implemented platforms processing 1PB+ datasets for global enterprises.
Complete GCP Stack: BigQuery, Dataflow, Cloud Storage, Pub/Sub, Vertex AI, Looker Studio
Other Data Engineering Services I Offer
FAQ
How does BigQuery compare to traditional data warehouses?
Revolutionary advantages: query terabytes in seconds vs hours, pay only for data scanned (~$5/TB) vs fixed costs, automatic petabyte scaling, zero maintenance. I provide detailed cost/performance benchmarks.
Can you integrate GCP with existing AWS or Azure infrastructure?
Yes! I specialize in hybrid cloud: data transfer from AWS S3/Azure Storage, cross-cloud API integrations, secure networking (VPN/Interconnect), identity federation, and multi-cloud cost optimization.
How do you handle real-time analytics requirements?
Streaming-first architecture using Pub/Sub (millions of messages/second), Dataflow streaming transformation, BigQuery streaming inserts, Cloud Functions for event processing, and real-time Looker Studio dashboards.
What machine learning integration do you provide?
AI/ML-ready foundations: BigQuery ML for in-database training, Vertex AI pipeline integration, feature stores, automated model retraining, and real-time prediction serving through Cloud Functions.
How do you optimize BigQuery costs for large datasets?
Multiple strategies: partitioning and clustering (95% cost reduction), materialized views, query optimization, slot reservations vs on-demand pricing, and data lifecycle policies for cheaper storage tiers.
