Available for engagements

Besar Maxhuni

Building scalable ETL pipelines, data lakes, and distributed systems on AWS. Focused on cloud-native data platforms, analytics engineering, and FinOps-aware architectures.

01 — Expertise & Tech Stack

Data flow interface

Core capabilities across AWS data services, distributed processing, and cloud-native engineering — focused on building reliable, cost-aware data platforms.

AWS Data Services
Primary
S3

Data lake

Redshift

Warehouse

Glue

ETL / Catalog

Lambda

Compute

Athena

Ad-hoc query

Kinesis

Streaming

Stack
10 Tools
PythonSQLSparkKafkadbtAirflowDockerTerraformPostgreSQLPandas
FinOps

cost-aware design

Storage tiering · query optimization · right-sized compute

Education

M.S. CS

Big Data Science · ongoing

data-lake → warehouse → analytics·IaC + version-controlled pipelines

02 — Featured Projects

Active nodes

Operational data systems and analytical deep-dives — each ships with reproducible infrastructure or a documented query pipeline.

fraud-triage.servicePRODUCTION

Real-Time Fraud Triage System

End-to-end fraud detection workflow with a relational backbone and an interactive triage UI. Containerized for reproducible local and cloud deployment.

  • PostgreSQL schema for transactions and triage events
  • Streamlit dashboard for analyst review
  • Docker-compose stack for one-command bring-up
PythonPostgreSQLStreamlitDocker
ai-impact-2030.analysisCASE STUDY

AI Impact Jobs 2030

Exploratory analysis of a Kaggle dataset projecting AI's impact on the labor market through 2030. SQL-driven aggregation surfaces sector-level shifts and exposure trends.

  • Window functions for cohort-over-time comparison
  • Sector clustering by automation exposure
  • Reproducible SQL pipeline for re-runs
SQLKaggleData Analysis