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Hamza Zayn

@hamzazayn

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I build governed multi-cloud healthcare and FinTech data platforms, lakehouses, and real-time pipelines.

United States

Message

What I'm looking for

I'm looking to lead the design and delivery of scalable, governed cloud data platforms, especially for healthcare, analytics, and AI/ML workloads in regulated environments.

At GlobalPoint, I lead architecture and delivery for healthcare data platforms across AWS, Azure, and GCP, supporting clinical integration, analytics, reporting, and AI/ML.

I build production ETL/ELT and ingestion pipelines with Python, SQL, PySpark, Airflow, dbt, AWS Glue, and Azure Data Factory for governed EHR/EMR, HL7, FHIR, claims, and clinical data.

I design Databricks Lakehouse, Delta Lake, Snowflake, Redshift, BigQuery, and Azure Synapse solutions, with streaming through Spark, Kafka, Kinesis, and Flink. I also strengthen quality, lineage, HIPAA-aligned PHI protection, observability, and deployment automation.

Previously at ApTask and Collibra, I modernized transaction, lending, analytics, and integration pipelines, built reusable transformation layers, and partnered with engineering and business teams to deliver reliable data products.

Experience

Work history, roles, and key accomplishments

Current

Lead Data Engineer / Data Architecture Lead

Current

GlobalPoint

Nov 2022 - Present (3 years 10 months)

Lead architecture and delivery of scalable healthcare data platforms across AWS, Azure, and GCP, supporting clinical integration, analytics, reporting, AI/ML, and governed self-service data consumption. Design and build production-grade ETL/ELT and ingestion pipelines with Python, SQL, PySpark, Airflow, dbt, AWS Glue, and Azure Data Factory for structured and semi-structured healthcare data.

Senior Data Engineer

ApTask

Aug 2019 - Oct 2022 (3 years 2 months)

Built scalable ETL/ELT pipelines using Python, SQL, Apache Airflow, dbt, and AWS Glue for transactional, lending, reporting, and analytical workloads. Modernized legacy data flows on AWS using S3, Redshift, Glue, Lambda, Spark, and cloud-native patterns to improve scalability, maintainability, reliability, and cost efficiency.

Python SQL Airflow DBT AWS Glue S3 Amazon Redshift AWS Lambda Spark PySpark Kafka Snowflake Terraform GitHub Actions CI CD DataHub Datadog Cloudwatch

Data Engineer

Collibra

Jun 2015 - Jul 2019 (4 years 1 month)

Built reusable ETL pipelines ingesting structured and semi-structured data from relational databases, REST APIs, JSON services, files, and SaaS applications. Developed Python and SQL transformations for cleansing, normalization, enrichment, deduplication, validation, and analytics-ready dataset preparation.