# Voranipit Chaipinitnorachart

Data Engineer focused on scalable data platforms, production reliability, and distributed processing systems.

Experienced in building and supporting enterprise data workflows across Azure, AWS, Databricks, and modern orchestration frameworks.

Currently working on:
- Databricks optimization
- Reusable ELT platform architecture
- Data quality & observability
- Cost-efficient distributed systems
- AI + Data Infrastructure

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## Tech Stack

### Core
Python • SQL • PySpark

### Data Engineering
Airflow • dbt • Kafka • Databricks

### Cloud Platforms
AWS (S3, Redshift, EC2)
Azure (ADF, Synapse, APIM, Key Vault)

### Platform & DevOps
Docker • CI/CD • CDKTF • MLflow

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## Enterprise Experience

### Sertis
Building and supporting enterprise-scale data platforms and distributed processing systems.

Key contributions:
- Developed Unified Data Platform components for enterprise clients
- Built Synapse + PySpark pipelines across multiple data sources
- Implemented orchestration workflows using Azure Functions and ADF
- Supported CI/CD standardization using CDKTF
- Refactored and modularized Databricks workflows for maintainability
- Contributed to governance and integration workflows across Azure services

### Enterprise Client Projects
Contributed to enterprise data initiatives across multiple organizations.

Selected work:
- Refactored Databricks pipelines into reusable modular components
- Packaged shared logic into reusable Python libraries
- Standardized engineering workflows and deployment patterns
- Implemented secure API integration workflows using Azure services
- Supported governance and access control initiatives
- Built orchestration workflows involving distributed systems

### Sunday Technology
Worked on production-grade ELT systems supporting multiple business domains.

Key contributions:
- Maintained ELT frameworks supporting multiple data sources
- Built orchestration pipelines using Airflow and dbt
- Developed Kafka ingestion pipelines for near real-time workflows
- Built reusable feature engineering pipelines for ML workflows
- Implemented schema change detection and monitoring systems
- Developed PII masking and decryption workflows across environments
- Resolved production incidents across pipelines and distributed systems

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## Engineering Principles

I optimize for:
- maintainable pipeline architecture
- modular distributed systems
- production reliability
- cost-aware data processing
- observability and data quality
- reusable engineering patterns

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## Selected Problems Solved

- Reduced duplicated logic by modularizing Databricks workflows into reusable libraries
- Improved deployment consistency using infrastructure-as-code workflows
- Built schema change detection systems to reduce production incidents
- Standardized feature pipelines for machine learning workflows
- Supported reverse ETL and distributed ingestion systems
- Resolved production data issues across multiple environments

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## Current Focus

Currently building:
- production-grade ELT frameworks
- scalable PySpark workflows
- data platform tooling
- Databricks optimization practices
- reusable engineering systems

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## Connect

- LinkedIn: www.linkedin.com/in/voranipit-chaipinitnorachart-a620a81a5