I specialize in Snowflake architecture design, warehouse right sizing, query optimization, clustering strategy, workload isolation, and Cortex AI integration. With 10+ years in data engineering and deep Snowflake expertise, I help organizations eliminate waste, improve performance, and build scalable, cost-efficient cloud data platforms.
I’m Ateeq ur Rehman, a Snowflake Cost & Performance Optimization Specialist with over 10 years of experience in data engineering and analytics architecture. I help organizations reduce Snowflake compute costs, improve warehouse performance, and design scalable, efficient cloud data platforms.
My work focuses on identifying architectural inefficiencies, optimizing workloads, and implementing best practices that drive measurable performance gains and cost savings. Over the years, I’ve worked across diverse data ecosystems from traditional relational systems to modern cloud-native platforms. This background enables me to approach Snowflake environments with both deep technical precision and strategic architectural insight.
I deliver enterprise-grade Snowflake solutions that help organizations modernize their data platforms, reduce costs, and accelerate analytics.
Certified Snowflake professionals delivering enterprise-grade implementations following industry best practices.
Accelerate implementations and migrations using proven delivery frameworks.
Reduce warehouse costs with query tuning, workload optimization and governance.
Experience building secure, scalable cloud data platforms for enterprise workloads.
Build AI-powered analytics using Snowflake Cortex, RAG, document intelligence and LLMs.
From architecture and migration to optimization and ongoing managed support.
Build AI-powered business agents that can understand data, answer business questions, generate insights, and automate routine workflows, helping teams make faster, data-driven decisions.
Connect AI agents with your business data to answer questions, generate insights, detect exceptions, and automate business workflows. For example, a Customer Retention AI Agent can identify customers at risk of leaving, analyze their behavior and history, and recommend proactive actions to improve retention.
This project demonstrates a modern data pipeline for e-commerce analytics, designed to integrate, process, and visualize large volumes of transactional and behavioral data. Data from operational databases is ingested using Hevo, while API and log data are streamed through Apache Kafka and Apache Flume for real-time capture. All raw data is centralized and transformed within Snowflake using dbt and Airflow to build curated analytical layers. Finally, Power BI dashboards deliver rich insights into customer behavior, sales performance, and operational trends — enabling data-driven decision-making across the business.