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Asad Qureshi

@asadqureshi2

Senior Data Engineer at Athenahealth who cut clinical workload runtime by 71% and reduced compute cost by 54%.

At Athenahealth, I designed and operated Hadoop-based batch and streaming pipelines for clinical, claims, and payer data. I evaluated incremental processing for clinical-document workloads, reducing runtime by 71% and compute cost by 54%; the framework was adopted across 12 additional analytics and NLP pipelines.

I also consolidated healthcare data feeds, tuned Spark and Hive workloads, and helped teams get faster access to reporting through Trino on Kubernetes. Earlier, at IBM, I modernized insurance data workflows, and at Montway Auto Transport, I worked on ETL and streaming systems for transportation data.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer - Hadoop

Current

Athenahealth

May 2019 - Present (7 years 6 months)

Designed and operated Hadoop-based batch and streaming pipelines for clinical, claims, and payer data on AWS EMR, Azure HDInsight, and Databricks, processing 4.3M+ annual transactions. Evaluated incremental versus full-batch processing, achieving a 71% runtime reduction and 54% lower compute cost, and standardized on Delta/Iceberg for improved refresh reliability.

Big Data Engineer

IBM

Nov 2015 - Apr 2019 (3 years 5 months)

Developed forecasting models for policy renewals and claims volume across a $180M+ commercial insurance portfolio, and built customer-risk segmentation models that improved renewal rates by 7.4%. Re-architected underwriting data-preparation workflows, reducing processing time from over 10 hours to under 3, and introduced event-driven ingestion with Kafka, cutting reporting latency from 4 hours to

Hadoop HDFS Hive Sqoop Apache Spark Kafka Airflow Python Scala SQL Snowflake Redshift GitLab CI CD XGBoost Random Forest Monte Carlo Power BI Tableau

Data Engineer

Montway Auto Transport

Jun 2013 - Oct 2015 (2 years 4 months)

Consolidated dispatch, booking, and transportation datasets into centralized ETL workflows using Hadoop, Spark, Hive, and Sqoop, reducing nightly processing windows from over four hours to under 90 minutes. Supported development of Kafka and Flink streaming pipelines and contributed to Redshift reporting environments for route efficiency and transportation performance metrics.