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// system online

Aishah Abdul-Hakeem — Analytics Engineer

> Analytics Engineer

Turning messy data into pretty pipelines.

GET IN TOUCH↓ ResumeGitHubLinkedIn
// 01 — about.md

About Me

I build systems that turn messy data into decisions. Currently completing my MS in Data Analytics & Visualization at Morgan State, with a Cornell University CS foundation and real production experience across pipelines, dashboards, and automation. I'm not looking for just any analytics role, I'm building toward engineering leadership at the intersection of data, systems, and scale.

loc: Baltimore, MDmail: aishah.a1809@gmail.com
// 02 — stack.json

Skills & Stack

Languages
Python95%
SQL92%
R78%
JavaScript75%
Java72%
Swift68%
C65%
OCaml60%
MATLAB65%
SAS63%
Assembly55%
Visualization
Tableau88%
Power BI85%
Streamlit90%
Plotly85%
Data & Pipelines
pandas / NumPy92%
ETL Pipelines88%
Airtable85%
Google Analytics80%
Automation
n8n90%
Zapier82%
WhatsApp API85%
Webhook Pipelines83%
Cloud
AWS75%
Supabase80%
GitHub90%
ML & AI
scikit-learn80%
NLP / LLMs78%
Machine Learning82%
// 03 — career.log

Positions Held

Analytics Engineer Intern

June 2025 – June 2026
@ Cornell University, AHDC-QMPS · Hybrid, Ithaca, NY
  • ▸Automated dairy health data workflows using Python (pandas, NumPy) and Streamlit, cutting processing time from 1 day to under 2 minutes.
  • ▸Designed and deployed end-to-end data pipelines to clean, standardize, and aggregate raw field data across farms.
  • ▸Built interactive dashboards visualizing hygiene metrics for client and internal use.
  • ▸Vibe coded a data entry app for data collection using lovable
  • ▸Architected a scalable webhook pipeline using n8n and Meta WhatsApp Cloud API for real-time message processing at scale.
  • ▸Designed a bilingual WhatsApp-based training platform using Airtable, Supabase, Cloudinary, and Lovable.
  • ▸Leveraged Google Analytics to track user behaviors and Airtable to track user status to trigger automations.

AI Challenge Participant — AI Architect & Technical Lead

March 2025 – April 2025
@ MoVi Hackathon · Remote
  • ▸Implemented a quantitative forecasting model for U.S. grid replacement integrating asset retirements, demand projections, and investment timelines.
  • ▸Awarded AI Architect and Technical Leadership Excellence out of 40+ participants.
  • ▸Led cross-functional team using Claude.ai and Perplexity; delivered visualizations via Plotly.ai and Lovable.
// 04 — portfolio/

My Portfolio

Field Data Cleaner

shipped

Streamlit app that cleans uploaded CSV and Excel files by standardizing column names, removing empty rows, flagging missing values and duplicate records, and exporting a cleaned workbook with a data quality summary. Built as a public, sanitized version of a larger reporting automation workflow from my internship.

PythonStreamlitPandasOpenpyxlData Quality
→ repo

Skincare Compatibility Checker

shipped

ML app that predicts whether a Sephora product is safe to add to your skincare routine. Gradient Boosting + VADER sentiment on 1M+ reviews across 2,400 products.

Pythonscikit-learnStreamlitVADERpandas
→ repo

Farm Learning — Bilingual Training Platform

shipped

WhatsApp-based training system for dairy farm milkers. Flip cards with EN/ES audio, Airtable backend, automated certificate delivery, deployed for real farm workers.

n8nAirtableWhatsApp APIJavaScriptSupabase

Deep Learning with Small Data

shipped

Replicated key experiments from Brigato & Iocchi (2020) on CNN performance under data scarcity. Found augmentation hurts at N=10 on Fashion-MNIST — an interesting exception the paper noted but didn't emphasize.

PyTorchPythonCIFAR-10Google Colab
→ repo