Waleed Ch

Lead Data Engineer with 9+ years of experience designing and delivering production-grade cloud data platforms on AWS, GCP, andAzure. Builds end-to-end systems spanning ingestion, batch and streaming ETL/ELT, data lakes, lakehouses, and warehouses using Python, SQL, PySpark, Kafka,Airflow, Databricks, and Snowflake, backed by Terraform and CI/CD.

Proven results: 40% lower ETL latency, 3x pipeline throughput, up to 5x faster queries, and 60% fewer production incidents. Leads and mentors engineers and partners with analytics and productteams to deliver trusted, cost-efficient data for decision-making.

Lead Data Engineer - Evolve Squads

(2023-09)

• Led architecture and delivery of cloud-native data platforms processing multi-terabyte daily datasets across AWS and GCP, improving data availability for analytics and BI.
• Built resilient data ingestion frameworks for APIs, third-party feeds, Cassandra, Hadoop, and databases, consolidating 15+ sources into a centralized data lake.
• Developed Spark/PySpark transformation pipelines and Airflow workflows, reducing ETL latency by 40% and increasing throughput by 3x.
• Optimized storage, partitioning, indexing, and query strategies, improving query performance by up to 5x while reducing cloud compute costs.
• Implemented data quality, validation, monitoring, and reconciliation frameworks, decreasing production incidents by 60%.
• Designed scalable ETL/ELT pipelines and dimensional data models supporting enterprise analytics, reporting, and BI workloads.
• Led adoption of Terraform, Infrastructure as Code, and CI/CD practices, reducing provisioning and deployment time by approximately 75%.
• Mentored data engineers on Spark, Airflow, cloud architecture, IaC, CI/CD, and data engineering best practices.

Senior Data Engineer - Dell Technologies (SecureWorks)

(2017-01 - 2023-08)

• Designed and implemented scalable ETL pipelines using Python, SQL, and cloud-native services for analytics and operational workflows.
• Engineered Hadoop and Spark batch processing pipelines for high-volume datasets, reducing job runtimes by 30%.
• Integrated Cassandra, relational databases, APIs, and enterprise applications into centralized analytical data platforms.
• Developed SQL transformations, data models, and reusable processing workflows supporting reporting and downstream analytics.
• Automated pipeline scheduling and dependencies using Apache Airflow, improving workflow reliability and operational efficiency.
• Optimized Spark jobs and SQL queries through partitioning, indexing, caching, and query tuning to improve performance.
• Supported data validation, cloud migrations, production troubleshooting, testing, and deployment in collaboration with senior engineers and analytics teams.

BSc - Computer Engineering - Brown University (2015-12)