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Entity X
3 months
Marketing and advertising
North America
Entity X is a data-driven organization focused on delivering scalable analytics and automation solutions to its clients across various industries. By integrating and optimizing cloud-based data infrastructure, Entity X empowers businesses to extract insights efficiently, reduce operational costs, and scale their data processing workflows. Their offerings blend modern ETL architecture, cloud deployment, and automation to streamline enterprise-level data management.
Cloud Run ETL with auto scaling enabled
BigQuery cost optimized by partitioning
Reporting workflows automated with App Script
Containerized architecture for deployment monitoring
Data Engineer
BigQuery Specialist
Cloud DevOps Engineer
Automation Engineer
Hourly Contract
Eliminating Data Gaps, Inefficient Reporting, and Weak Customer Understanding
Legacy workflows struggled with performance under scale
Unoptimized storage and lack of partitioning increased GCP spend
Time-consuming report preparation and file handling
To address growing data volume and cost concerns, we focused on accelerating ETL pipelines using serverless architectures that eliminate the need for dedicated infrastructure. Optimizing BigQuery with partitioning and clustering strategies minimized compute and storage expenses. Automation of repetitive data tasks streamlined workflows, reducing manual effort and error risk. This approach ensures scalable, cost-effective data processing, enabling faster insights and better resource utilization to support business growth and agility.
● How can we accelerate ETL pipelines for high-volume data?
● What architecture minimizes BigQuery compute and storage costs?
● Can we scale without provisioning dedicated infrastructure?
● How do we automate repetitive data management tasks?
Optimized ETL Architecture for Scalability and Cost Efficiency
We containerized ETL jobs using Docker, deploying them on Cloud Run to leverage serverless scalability and reduce infrastructure overhead. BigQuery tables were optimized through partitioning and clustering, significantly lowering query costs and improving performance.
Google Workspace data workflows were automated using App Script, eliminating manual steps and streamlining operations. Additionally, Dataform was employed to manage complex data transformation logic, ensuring maintainable, reusable SQL pipelines.
This combination of technologies resulted in a robust, scalable, and cost-effective data processing system that accelerates analytics delivery while minimizing operational expenses.
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increase in data throughput with optimized ETL workflows.
reduction in storage costs through improved data management practices.
hours/week saved by automating manual data tasks.
boost in operational efficiency, enabling focus on high-impact initiatives.
DataRopes.ai is a global technology partner which delivers end to end services like data engineering, data analysis, data science and AI & ML
Reviewed on
150 E B St Lbby # 1810
1106 A, 11th floor, Haly Tower,
Sector R DHA Phase 2, Lahore
DataRopes.ai is a global technology partner which delivers end to end services like data engineering, data analysis, data science and AI & ML.
Reviewed on
Address
150 E B St Lbby # 1810
1106 A, 11th floor, Haly Tower,
Sector R DHA Phase 2, Lahore
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