Data Engineering | Machine Learning | Data Visualization | IoT Analytics
I help businesses turn complex data into reliable, scalable, and actionable systems.My focus is on end-to-end data solutions — from raw data ingestion to real-time insights and automated decision-making.
1. Data Engineering & Pipeline Automation
Design and build robust, high-performance ETL/ELT pipelines using Databricks, Azure, and PySpark.
Develop data lakehouse architectures optimized for performance and cost.
Integrate multiple data sources (APIs, SQL, Kafka streams) into unified analytical environments.
Implement monitoring and error-handling to ensure data reliability and consistency.
Deliverables:
- Automated data pipelines (batch & streaming)
- SQL optimization and database refactoring
- Data lakehouse implementation (Delta Lake / Parquet)
- Azure Data Factory or Airflow orchestration
2. Machine Learning & Advanced Analytics
Build predictive and anomaly detection models for fraud prevention, customer segmentation, and risk scoring.
Deploy end-to-end ML workflows with model version control and CI/CD integration (MLOps).
Use unsupervised and deep learning methods for complex pattern discovery.
Deliverables:
- Trained and documented ML models ready for deployment
- Automated ML pipelines (MLflow, scikit-learn, TensorFlow)
- Analytical dashboards showing model performance and insights
3. Data Visualization & Storytelling
Design interactive, executive-level dashboards in Power BI or Plotly Dash.
Translate analytical findings into clear, data-driven narratives for stakeholders.
Build real-time monitoring systems with visual alerts and KPIs.
Deliverables:
- Interactive dashboards (Power BI / Dash / Streamlit)
- Data storytelling reports and business presentations
- Custom data visualizations for operational or analytical use
4. IoT & Real-Time Data Processing
Implement real-time analytics pipelines for sensor data (Spark Streaming + Kafka).
Develop anomaly detection and predictive maintenance systems for IoT environments.
Build end-to-end workflows: from sensor ingestion → feature extraction → visualization.
Deliverables:
- Streaming data pipelines for IoT signals
- Predictive maintenance models
- Real-time dashboards and alerts
5. Data Science Consulting & Prototyping
Rapid prototyping of data-driven products or research models.
Exploratory data analysis, feature engineering, and business-driven insights.
Technical documentation and mentoring for internal data teams.
Deliverables:
- Data analysis reports with actionable insights
- Working prototypes for proof-of-concept validation
- Complete technical documentation and reproducible code
Tech Stack
Languages: Python, SQL, PySpark
Platforms: Databricks, Azure, Airflow, MLflow, Power BI
Libraries: Pandas, NumPy, scikit-learn, TensorFlow, Plotly Dash
Tools: Delta Lake, DevOps, Git