Mohammed-Taqi Jalil
Data Engineer
Austin, TX · 512-696-1614 · mohammed.jalil.email@gmail.com · linkedin.com/in/mohammedtaqijalil · github.com/MohammedJalil
[01] Summary
Data Engineer who helped take an AI-powered records-adjudication platform from prototype to production. Own the platform's core data transformation service (Python/FastAPI) and work across the stack: API development, data validation, BigQuery pipelines, automated testing, and internal tooling.
[02] Experience
Data Engineer
January 2026 - PresentSNH AI · Austin, TX
- Own the Python/FastAPI service that transforms raw court-record XML from multiple vendors into the structured features driving automated compliance decisions.
- Helped take the product from prototype to production launch, supporting multi-week client UAT and shipping 100+ merged pull requests across parsing, enrichment, validation, and decision logic.
- Implemented jurisdiction-specific decision logic (offense age, dispositions, court exclusions) with pytest coverage, expanding the share of records adjudicated without manual review.
- Diagnosed production incidents by tracing records through a BigQuery bronze/silver/gold pipeline, fixing parser failures and ID-mapping bugs.
- Built internal React dashboard features (record search, labeling, live rule evaluation) used daily by engineers and auditors.
Data Engineer Intern
June 2025 - January 2026SNH AI · Austin, TX
- Built data labeling workflows in Label Studio for annotating charges, dispositions, and record sources used to train and evaluate the platform's AI models.
- Developed Python pipelines standardizing heterogeneous vendor XML into a unified golden dataset for model training and evaluation.
- Created and validated XSD schemas to enforce consistency of incoming XML across diverse data sources.
- Converted to full-time after the internship, taking ownership of the platform's core transformation service.
Data Analyst Intern
February 2024 - August 2024JSoftUSA · Austin, TX
- Analyzed student attendance and performance data, uncovering trends that led to a 15% increase in platform adoption.
- Automated ETL workflows using SQL scripts, decreasing manual report time by 30%.
- Built Tableau dashboards to track performance and resource use, improving educator efficiency.
[03] Skills
Languages & Frameworks: Python (FastAPI, Pandas, pytest), SQL (BigQuery, MySQL), TypeScript/React, XML/XSD, REST APIs
Cloud & Data: Google Cloud Platform (BigQuery, Cloud Run, Pub/Sub), ETL / medallion architecture (bronze-silver-gold), data validation, Docker, Git/GitHub, Label Studio, Tableau
[04] Projects
Ambient Air Pollution Prediction (Python, Scikit-learn): ML models (Random Forest, XGBoost, k-NN, Lasso) predicting PM2.5 across the U.S.; EDA, feature selection, and cross-validation.
IntelliVest Investment Analytics Platform: investment analytics with real-time market data, portfolio optimization, and interactive dashboards.
RNA-Seq Gene Expression Analysis (R, Bioconductor): differential expression with DESeq2, PCA/volcano/heatmap visualizations.
[05] Education
The University of Texas at Austin · B.S. Biology (Computational) · May 2024