Open to Cloud Data Engineering & Data Roles

Hi, I'm Parsa
Kamali.

I focus on Cloud Data Engineering, data pipelines, analytics, and machine learning workflows. I work with Python, SQL, AWS, Databricks, Spark, and modern data tools to build reliable, scalable, and analytics-ready data solutions.

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Parsa Kamali

Cloud Data Engineer

Data Engineering • Analytics • ML • AWS • Databricks • Python • SQL

About Me

I develop data-driven solutions with a strong focus on Cloud Data Engineering, data pipeline design, analytics, and machine learning workflows. My skill set combines Python, SQL, AWS, Databricks, Spark, data processing, and software development, enabling me to contribute to Data Engineering, Data Analytics, and Data Science projects.

Data Engineering

ETL/ELT pipelines, SQL, data modeling, batch processing, data quality, and cloud-based data workflows.

Cloud & AWS

AWS S3, Glue, Athena, Redshift, Lambda, Step Functions, IAM, and cloud data architecture concepts.

Databricks & Spark

Databricks Lakehouse, Apache Spark, PySpark, Delta Lake, Lakeflow Jobs, and scalable data processing.

Analytics & ML

Data analysis, feature engineering, visualization, classification, model evaluation, and machine learning workflows.

Experience

A focused overview of my practical experience across cloud data engineering, data science, AI data quality, and software development.

2026 — Present

Cloud Data Engineering & Lakehouse Development

Building practical skills in cloud data engineering with AWS, Databricks, Spark, Delta Lake, and ETL/ELT pipeline design. Focus areas include data ingestion, transformation, orchestration, data quality, analytical storage, and scalable lakehouse architecture.

2024 — Present

AI Data Quality & Evaluation

Working with AI-related data tasks involving content evaluation, quality assessment, structured annotation, and analytical review. This experience strengthens my attention to data quality, consistency, multilingual content, and reliable evaluation processes.

2024 — 2026

Data Science & Machine Learning Projects

Completed academic and independent projects involving large-scale datasets, statistical analysis, machine learning, graph-based modeling, text data processing, and model evaluation using Python, SQL, PyTorch, XGBoost, and data visualization tools.

Previous

Backend & Database Development Background

Developed backend-oriented web applications and database-driven systems using PHP, SQL, SQL Server, HTML, CSS, Bootstrap, JavaScript, jQuery, and AJAX. Worked with database structures, dynamic pages, data handling, and application logic connected to relational databases.

Projects

Selected academic and independent projects in data engineering, machine learning, graph analytics, statistical modeling, and cloud-oriented data workflows.

Retail Demand Forecasting & MLOps Lakehouse

Building a production-oriented retail forecasting platform with AWS S3, Databricks, PySpark, Delta Lake, MLflow, leakage-safe 28-day forecasting, Power BI, FastAPI, Docker, and automated tests.

AWS Databricks PySpark Delta Lake MLflow FastAPI

E-Commerce Lakehouse Purchase Prediction

Building an end-to-end cloud lakehouse pipeline with AWS S3, Athena, Databricks, PySpark, Parquet, and Spark ML for session-level purchase prediction.

AWS Databricks PySpark Athena Parquet

Eye Tracking

Exploring age-related and temporal patterns in eye-tracking fixation data using statistical modelling.

Eye Tracking Statistics Fixation Data TU Dortmund

World Value Survey

Analyzing political trust, cultural values, country differences, and time trends using World Values Survey data.

WVS Political Trust Ordinal Regression Time Series

Gravitational Wave SBI

Estimating binary black hole source parameters from simulated gravitational waveforms using PyCBC, BayesFlow, and Flow Matching.

SBI BayesFlow PyCBC Flow Matching

Telegram Censorship GNN

Predicting Telegram message deletion using graph neural networks, behavioral features, temporal patterns, and reply-chain structure.

GNN GATv2 Telegram Censorship

BOSCH Predictive Maintenance

Detecting anomalies in wire-cutting torque time-series data using DTW, FastDTW, and VAE-LSTM models.

Time Series Anomaly Detection VAE-LSTM DTW

Survival Analysis

Analyzing censored survival times of ice cream machines using Cox models, Nelson-Aalen estimation, and time-dependent covariates.

Survival Analysis Cox Model Counting Processes R

Statistics Meets Linguistics

Analyzing adjective usage in political media coverage using Politico 28 data, linguistic tags, decision trees, and statistical testing.

NLP Linguistics Decision Tree Python

Anglicism Survey Analysis

Analyzing the frequency and motivations of Anglicism usage among university students using survey data, statistical testing, and interpretable models.

Survey Analysis Linguistics Decision Tree Python

Demographic Data Analysis

Analyzing global life expectancy and under-5 mortality rates across countries, regions, sexes, and time periods using descriptive statistics.

Descriptive Statistics Demography Python Visualization

Birth Weight & Smoking Analysis

Comparing baby birth weights across maternal smoking-status groups using descriptive statistics, global testing, Bonferroni correction, and Tukey HSD.

ANOVA Tukey HSD Bonferroni Python

Seoul Bike Regression

Modeling Seoul bike-sharing demand with linear regression, AIC-based variable selection, residual diagnostics, and VIF analysis.

Regression AIC VIF R

Skills

A focused technical skill set across cloud data engineering, analytics, machine learning, and software development.

Python
SQL
AWS
Databricks
Apache Spark
ETL/ELT Pipelines
Delta Lake
Data Analytics
Machine Learning
Power BI
Git & GitHub
PHP & Backend

Contact

Want to discuss a role, project, or collaboration? Send me a message.

parsa.kamali.data@gmail.com

linkedin.com/in/parsa-kamalii

github.com/Parsaka

Bochum, NRW, Germany