Hi 👋, I'm Muh Mubeen !
Nice to meet you.

Senior Data Solution Architect with 11+ years of experience designing scalable cloud-native data platforms, real-time analytics systems, and AI-ready Lakehouse architectures across AWS, Azure, and GCP. Expertise in Databricks, Snowflake, Spark, Kafka, ETL/ELT pipelines, and data warehousing, with a strong track record of optimizing performance, reducing infrastructure costs, and delivering enterprise-scale data solutions across healthcare, finance, and cloud ecosystems.

Muh Mubeen portrait

Skills

A quick snapshot of my toolkit

🐍
Python
98%
🛢️
SQL
96%
⚙️
Golang
88%
🐳
Docker
90%
🦜
Kafka
89%
🧩
Debezium
84%
⚡
Spark
93%
🧠
LangChain
92%
🤗
HuggingFace
91%
📚
RAG
94%
📈
Scikit-Learn
97%
☁️
AWS
90%
🌐
GCP
88%
🖥️
Azure
86%
🗄️
MySQL
92%
🐘
PostgreSQL
94%
🍃
MongoDB
89%
❄️
Snowflake
87%
🧱
Databricks
85%
📊
Tableau
95%
📈
Power BI
96%
🔄
Alteryx
83%
⚙️
Talend
82%
⚡
FastAPI
90%

Experience

Data Solution Architect

2022.10 - Present

Technologies

Apache KafkaAWS (EC2, Lambda)Apache FlinkAWS KinesisGCPAzureAWSDelta LakeDatabricksSnowflake

Highlights

    Architected scalable cloud-native data platforms across AWS, Azure, and GCP using Databricks, Snowflake, and Kafka.
    Designed real-time streaming pipelines with Apache Kafka, Flink, and AWS Kinesis, improving operational responsiveness by 35%.
    Led enterprise cloud migration initiatives, reducing infrastructure costs by 50% and improving query performance by 60%.
    Developed AI/ML-ready Lakehouse architectures using Microsoft Fabric, Delta Lake, and Databricks.
    Built and optimized 250+ ETL/ELT workflows using Airflow, Talend, Informatica, and Apache NiFi.
    Implemented enterprise-wide data governance, HIPAA/GDPR compliance, and secure access management.
    Mentored cross-functional engineering teams and implemented CI/CD automation using Jenkins, Docker, Kubernetes, and Terraform.

Senior Data Engineer

2019.08 - 2022.09

Technologies

MLflowDatabricks Apache Airflow Apache NiFi Amazon S3 HBaseHDFSHiveKafka Spark Hadoop A/B TestingTime Series Modeling

Highlights

    Developed cloud-scale ELT pipelines using Apache Spark, Snowflake, and Apache Airflow for enterprise analytics.
    Implemented Data Mesh-aligned architectures to improve distributed data ownership and reliability.
    Optimized Apache Spark workloads, significantly reducing query execution times and improving scalability.
    Automated ingestion and transformation pipelines using Apache NiFi and Airflow across multi-cloud environments.
    Supported ML workflows using Databricks and MLflow by delivering feature-ready datasets.
    Built CI/CD pipelines with Jenkins and GitLab CI for faster and reliable deployments.
    Led cloud data warehouse migrations and enabled real-time reporting solutions

Data Engineer

2017.06 - 2019.07

Technologies

REST APIsGreat Expectations PythonApache Beam Google Cloud Dataflow

Highlights

    Designed and implemented scalable cloud-based data lakes on Google Cloud Platform (GCP).
    Developed automated data quality validation frameworks using Great Expectations, reducing discrepancies by 40%.
    Integrated REST APIs, flat files, and cloud databases into centralized analytics platforms.
    Standardized enterprise data models and schema designs for improved reporting consistency.
    Collaborated on cloud infrastructure automation and CI/CD implementation using Terraform.
    Supported healthcare analytics and precision oncology research through scalable data engineering solutions.

Projects

SafeStreets

Data Solution Architect

Regulatory complianceAWS Apache Airflow Apache NiFi Apache Kafka
HIPAA-compliant data pipelines

Designed and led the development of a real-time healthcare analytics platform integrating EHR and claims data using Apache Kafka, Apache Flink, and AWS Kinesis.
Enabled predictive insights for population health management and reduced data processing latency by 60%.
Deployed HIPAA-compliant data pipelines with Apache NiFi and Airflow on AWS, enhancing care quality and regulatory compliance.

FinSight

Cloud Data Lakehouse Migration

MLflowMachine learning models TalendETL workflows Microsoft Azure Delta Lake Databricks

Led the migration of legacy on-premises data infrastructure to a unified cloud-based lakehouse using Databricks and Delta Lake on Azure.
Streamlined ETL workflows using Apache Spark and Talend, improving data refresh rates by 70%.
Integrated machine learning models with MLflow to forecast energy demands, increasing predictive accuracy by 30%.

DocuQuery

Financial Data Pipeline Modernization

Microsoft AzureData quality checks Data validation Data architecture Cloud-native data lake

Developed scalable ETL pipelines with Apache Beam, Python, and Google Cloud Dataflow, processing over 10 million financial records daily.
Designed a cloud-native data lake on GCP, enabling seamless access to structured and unstructured data for cross-team analytics.
Implemented automated data validation and quality checks using Great Expectations, reducing data inconsistencies by 40%.

Sue-per Bot

ML Feature Store for Fraud Detection

Real-time feature engineeringFraud detection Model iteration FeastMLflowDatabricks

Designed and deployed a centralized ML Feature Store using Databricks, MLflow, and Feast, enabling 3× faster model iterations. Reduced fraud detection false positives by 18% through real-time feature engineering.