Looking for a job

Pallets User

@palletsuser

Data Engineer at Amdari building AWS data pipelines that cut processing latency by 30% and cloud costs by £10,000 per quarter.

Canada

Message

At Amdari, I designed and implemented scalable cloud data pipelines using Spark on AWS EMR and Airflow, reducing processing latency by 30%. I also optimized storage and warehousing to save £10,000 per quarter.

At 10Alytics, I developed Python, SQL, and Airflow ETL pipelines that increased processing efficiency by 30%. I improved PostgreSQL query latency by 40% and used PySpark to accelerate big data transformations.

On Project RISING, I led ETL pipeline design for climate and public health datasets, processing 1,690 records. I also developed FastAPI services that reduced data retrieval time by 60%.

Experience

Work history, roles, and key accomplishments

Current

Lead Data Pipeline Engineer

Current

Project RISING (10Alytics Global Hackathon 2026)

Jul 2026 - Present (4 months)

Led design and delivery of ETL pipelines for climate and public health datasets that validated, cleaned and processed 1,690 records for downstream analytics using Python. Developed FastAPI-based REST services to expose health intelligence endpoints, reducing data retrieval time for consumers by 60%.

Python fastAPI ETL Pipeline Development REST APIs Cloud Architecture Documentation

Current

Data Engineer Internship

Current

Amdari

May 2026 - Present (6 months)

Designed and implemented scalable cloud data pipelines that reduced processing latency by 30% across client environments, using Spark on AWS EMR and Airflow orchestration. Architected storage and warehousing optimizations that cut infrastructure costs by £10,000 per quarter.

Apache Spark Amazon EMR Airflow Kafka Kinesis Data pipeline design Cost Optimization Query Optimization

Data/Cloud Engineer

10Alytics

May 2026 - Oct 2026 (5 months)

Increased data processing efficiency by 30% by developing scalable ETL pipelines using Python, SQL, and Apache Airflow, reducing daily pipeline execution time from 10 hours to 7 hours. Reduced PostgreSQL query latency by 40% through indexing, partitioning, and schema optimization.