Hani Al-Naber

Reduced pipeline execution time by up to 60% by optimizing critical ETL/ELT workloads across AWS, Databricks, dbt, Apache Airflow, and Airbyte through SQL tuning, DAG refactoring, incremental syncs, and parallelism improvements.
Partner directly with business and product stakeholders to translate requirements into reliable data products, leveraging AI agents (Amazon Q Developer, Claude Code, Databricks Genie) and Model Context Protocol (MCP) tooling to accelerate requirement discovery, data exploration, and execution.
Advance DataOps and DevOps maturity through GitHub Actions CI/CD, Terraform-managed infrastructure, automated dbt tests, and SLAs on Airflow pipelines, raising platform reliability and deployment velocity.

Independently owned end-to-end ETL/ELT pipeline development on AWS, Databricks, dbt, Apache Airflow, and Airbyte, shipping production-grade Python and SQL code for a financial data platform.
Developed and tuned dbt models and complex SQL transformations against AWS Athena, Databricks, and PostgreSQL, improving data quality and query performance for financial reporting use cases.
Used AI copilots (Amazon Q Developer, Claude Code, Databricks Genie) to prototype pipelines and translate analyst requirements into production data models; strengthened DataOps via GitHub-based CI/CD, code reviews, and automated dbt testing.

Ramped up on the AWS-based financial data platform, building initial dbt models and Python pipeline components under senior engineer mentorship and contributing to production releases.
Wrote and optimized SQL queries on AWS Athena, Databricks, and PostgreSQL; monitored Apache Airflow DAGs on AWS MWAA and configured Airbyte connectors for new data sources.
Leveraged AI assistants (Amazon Q Developer, Claude Code) to onboard quickly and translate business requirements into working code; contributed to Agile sprints using Jira and GitHub workflows.

Mentored and supported over 300 graduate and undergraduate students in technical subjects including Predictive Analytics and Business principles.
Clarified complex analytical concepts and translated technical material into understandable content.
Assisted faculty in developing and grading assignments, gaining deep exposure to the practical application of data analytics and machine learning methodologies.

Completed comprehensive training in environmental radioactivity and radiochemistry.
Gained foundational knowledge in nuclear engineering applications and safety protocols.