BODIN KRONGCHON
DATA ENGINEER
Hello, I'm

BODINKRONGCHON

DATA ENGINEER

Build • Operate • Improve

I build reliable, scalable, production-style data systems that turn real business data into trusted, actionable information.

View My Projects→
SQLPythonDatabricksPySparkAirflow
Portrait of Bodin Krongchon
8Completed ProjectsVerified repositories
✓
7/7Reliability LabsDatabricks
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193Automated TestsSQL project
DB
161Automated TestsInfluencer project
34Lineage EdgesSQL project
32Governance AssetsSQL project

Tech Stack

Python SQL Databricks PySpark Apache Airflow PostgreSQL SQLite Docker FastAPI GitHub Actions dbt BigQuery / GCP AWS Terraform
FEATURED PROJECTS

Completed Projects

Production-style data engineering projects with real-world business context and verified evidence.

View All Projects →
01SQL • ETL

SQL E-commerce
Data Engineering

Reliable SQL data platform with incremental load, data quality, monitoring, API serving, and recovery.

25Tables20Views57Indexes193Tests
SQLPythonAirflow
View Project →
02LAKEHOUSE

Databricks Lakehouse
Data Engineering

Databricks Lakehouse on Apache Spark with Delta Lake, Bronze-Silver-Gold, CDC, SCD2, data quality, reliability labs, CI, and measured Spark optimization.

7/7Labs5,249Rows25EvidencePASSPublic CI
DatabricksApache SparkDelta Lake
View Project →
03MASTER DATA

Influencer Master Data
& Campaign Intelligence

Governed influencer master data connecting source provenance, campaign history, and explainable matching decisions.

703Master161Tests2,519Aliases6,530Core
PythonSQLPostgreSQL
View Project →
04MARKETING

Marketing Campaign
Data Platform

Payment-data validation, rejected-record routing, SQLite analytics, and privacy-safe portfolio outputs.

268Records6OutputsDQGatesSAFEPrivacy
SQLPythonSQLite
View Project →
05PIPELINE

E-commerce
Data Pipeline

Multi-source Excel pipeline with validation, transformation, SQLite analytics, and portfolio-safe outputs.

105Records5OutputsDQSample PASSETLFlow
PythonPandasSQLite
View Project →
06GCP / BIGQUERY

BigQuery Enterprise
Data Platform

Production-oriented GCP/BigQuery platform with layered warehouse modeling, dbt, reconciliation, security, Terraform, and CI/CD.

32/32DoD12/12Runtime8RAW TablesPASSCI
BigQuerydbtTerraform
View Project →
07AWS / DATA PLATFORM

AWS Production Data Engineering
& Reliability Platform

AWS data platform for retail, vending, and payment workflows with SQL, Python, Glue, S3, Redshift, orchestration, data quality, and reliability engineering.

640TestsPASSCI0Safety FindingsPUBLICGitHub
AWSSQLRedshift
View Project →
08AIRFLOW / DBT

Airflow + dbt E-commerce
Data Platform

Production-oriented e-commerce platform with Airflow orchestration, PostgreSQL, dbt warehouse layers, data quality, monitoring, recovery drills, Docker, and CI.

8Sources257dbt Pass37/37PG TestsPASSHosted CI
AirflowdbtPostgreSQL
View Project →

Engineering Proof

Build beyond the happy path: data quality, monitoring, recovery, lineage, CI/CD, and performance evidence.

✓Data Quality193 tests ↗LineageEnd-to-end ⌁MonitoringSLA + alerts ↻RecoveryRCA + runbook ⌁PerformanceQuery tuning </>CI/CDValidation
View All Evidence →

Core Capabilities

Capabilities grounded in completed projects, from ETL and modeling to reliability and serving.

⇄ETL / ELTPipelines ◇Data ModelingStar + relational ⌘OrchestrationAirflow ⌁DocumentationContracts ⬡DevOpsDocker + CI ▥AnalyticsBusiness context
View Capabilities →

Experience

Data Engineer — E-commerceRAVE UP ADTECH AND SOLUTION Co., Ltd.

Real operational context across e-commerce, influencer, campaign, payment, and reporting workflows.

  • Multi-Platform Data Integration
  • Data Cleaning, Standardization & Deduplication
  • Client Insights & Business Recommendations
  • Payment Data Preparation & Reconciliation
View Full Experience →

Credentials

✓
Verified CertificateData Science Bootcamp Batch 12DataRockie · 2026VERIFIED
View Certificate →

“Data is not just numbers. It is better decisions.”

02 / PROJECTS

Engineering solutions to real data challenges

Eight portfolio projects shown with real pipeline runs, reliability screenshots, test output, CI checkpoints, and repository evidence. Metrics are recorded checkpoints, not a claim that every system is running live.

01

SQL E-commerce Data Engineering

A reliability-focused SQL platform with incremental loading, idempotency, quality and freshness gates, monitoring, recovery, Airflow, read-only API serving, Docker, and CI.

25Tables20Views57Indexes193Tests
GitHub Repository
02

Databricks Lakehouse Medallion Pipeline

A Databricks Lakehouse built on Apache Spark that demonstrates Delta Lake medallion architecture, CDC, SCD Type 2, data quality, controlled failure labs, monitoring, troubleshooting, CI, and measured Spark performance tuning.

7/7Labs5,249Rows25EvidencePASSCI
GitHub Repository
03

Influencer Master Data & Campaign Intelligence

A deterministic Master Data and PostgreSQL project that resolves fragmented influencer identities into a traceable Golden Master, preserves campaign and performance history, and supports explainable human-reviewed matching.

703Master2,519Aliases6,530Core Rows161Tests
GitHub Repository
04

Marketing Campaign Data Platform

Python/SQLite payment pipeline with rejected-record routing and six published outputs. Recorded DQ: 268 rows, 97 missing post dates, and 35 missing payment statuses; these are reported, not silently filled. Private source data is required for a full rerun; no hosted CI is configured.

268RecordsRejectedRoutingSQLite6Outputs
GitHub Repository
05

E-commerce Data Pipeline

Python/Pandas Excel ETL with SQLite analytics and five published outputs. The recorded 105-row sample has zero findings in its configured DQ report. Raw Excel files are private; Docker and hosted CI are not configured in this repository.

105RecordsDQPASSSQLite5Outputs
GitHub Repository
06

BigQuery Enterprise Data Platform

GCP/BigQuery warehouse with dbt, Terraform, incremental processing, reconciliation, governance and synthetic financial operations. Published runtime evidence records 12/12 checks and eight RAW tables. Public CI checks publication safety, Python syntax and Terraform formatting; it does not rerun the cloud workload.

32/32DoD12/12Runtime Proof8RAW TablesPASSPublic CI
GitHub Repository
07

AWS Production Data Engineering & Reliability Platform

A production-style AWS data engineering platform covering retail, vending, and payment data with SQL/Python pipelines, Glue and PySpark transformations, S3 landing, Redshift warehousing, dbt, Airflow, Terraform, data quality, monitoring, security, recovery, and CI/CD evidence.

640/640TestsPASSGitHub Actions0Safety FindingsPUBLICSanitized Portfolio
GitHub Repository
08

Airflow + dbt E-commerce Data Platform

Eight source contracts, PostgreSQL, Airflow and dbt with monitoring, recovery drills and Docker. Hosted CI checkpoint: 370 service-free tests, 37 PostgreSQL tests and 257 dbt build results passed. AWS infrastructure is reference/plan evidence; live ECS/RDS/ALB runtime is not deployed.

8Sources257dbt Pass37/37PG TestsPASSHosted CI
GitHub Repository
03 / ENGINEERING PROOF

I don't only build the happy path.

The portfolio proves that systems can be tested, observed, diagnosed, recovered, and verified — not only executed successfully.

DQ

Data Quality

Contracts, thresholds, reconciliation, quarantine, validation, and regression checks.

LG

Lineage & Governance

Dataset lineage, runtime lineage, asset registry, PII classification, masking, and hashing.

MON

Monitoring & Alerts

Run audits, step telemetry, SLA signals, alert fingerprints, occurrence history, and lifecycle.

RC

Recovery & Reliability

Controlled failures, retry, RCA, recovery contracts, backfill, and recovery validation.

PF

Performance Tuning

Query plans, benchmark evidence, indexing, Spark tuning, and regression protection.

CI

CI/CD & Reproducibility

GitHub Actions, automated tests, Docker validation, reproducible runtime checks, and release evidence before delivery.

DATA ENGINEERING OPERATING LOOP
Build→Operate→Break→Troubleshoot→Recover→Optimize→Explain
04 / CAPABILITIES

Core tools backed by completed project evidence.

Capability cards describe what I can build and operate today — not a future learning roadmap.

Core Tools

Python
SQL
PostgreSQL
Docker
Airflow
Databricks
SPPySpark
BQBigQuery
dbtdbt
AWSAWS
Git / CI
TFTerraform
ETL

Data Pipeline Development

Batch • Incremental • CDC • SCD

DM

Data Modeling

Relational • Warehouse • Medallion

OR

Orchestration

Airflow • Scheduling • Recovery

DQ

Data Quality

Validation • Reconciliation • Freshness

API

API & Serving

FastAPI • Read-only access • Safe outputs

CI

DevOps & CI/CD

Docker • GitHub Actions • Reproducibility

DB

Databases

SQL • PostgreSQL • SQLite

BI

Analytics & Business Context

E-commerce • Influencer • Campaign • Payment

MON

Observability & Reliability

Monitoring • Alerts • Retry • Recovery

CLD

Cloud & Infrastructure as Code

GCP/BigQuery • AWS • Terraform

05 / EXPERIENCE

Real business context behind the engineering projects.

Portfolio projects extend recurring operational data pain points into engineering labs. They do not claim that every portfolio technology is deployed in production at the company.

AUG 2025 – PRESENT

Data Engineer — E-commerce

RAVE UP ADTECH AND SOLUTION Co., Ltd.
  • Build and maintain recurring data pipelines from TikTok Shop, Shopee and Lazada exports.
  • Clean, validate, standardize and deduplicate operational datasets.
  • Analyze sales, advertising, SKU and campaign performance.
  • Prepare reconciliation data and reporting for business teams.
  • Reduce repetitive preparation through reusable automation.
RAVE UP
2026

Portfolio Engineering Labs

Data Engineering Projects
  • SQL, PostgreSQL, Airflow, dbt, Databricks, BigQuery and AWS project evidence.
  • Reliability labs, data quality, recovery, lineage and CI validation.
  • Eight completed portfolio projects with public repository evidence.
BK
AUG 2025PRESENT

Data Engineer — E-commerce

RAVE UP ADTECH AND SOLUTION Co., Ltd. · Bangkok, Thailand · Hybrid

CURRENT

I connect recurring operational data work with reusable engineering practices that reduce manual preparation and improve trust in analysis.

01

Data Integration & Organization

TikTok Shop, Shopee and Lazada data consolidated into reusable datasets and reporting workflows.

02

Data Quality & Reusable Data

Clean, standardize, validate and deduplicate influencer, campaign, sales, compensation and payment records.

03

Analytics & Optimization

Analyze sales, advertising, product, SKU and campaign performance to surface trends and opportunities.

04

Insights & Stakeholder Reporting

Turn campaign and commerce data into client insights, dashboards, presentations and next-action recommendations.

05

Payment & Cross-functional Operations

Prepare and reconcile payment data while supporting e-commerce, influencer and project operations in a lean team.

04
ACADEMIC BACKGROUND

Education

Formal education and structured learning that support my engineering practice.

2026

Data Science Bootcamp — Batch 12

The School of Generalist (DataRockie)

Hands-on data science and engineering practice through structured projects.

DR
2006–2010

Bachelor of Laws (LL.B.)

Burapha University

Legal foundation strengthened analytical reasoning, structured problem solving, and communication.

BU
06 / ABOUT

Business context first. Engineering tools second.

My portfolio connects hands-on e-commerce, influencer, campaign, payment, and reporting work with reliable data engineering. The goal is to reduce repetitive preparation, improve data trust, and give more time back to analysis and better decisions.

I prefer systems that are observable, recoverable, testable, and explainable — not just pipelines that happen to finish successfully.

◇E-commerce / Marketplace / Operational Data ◇Data Engineering / Data Platform / Reliability ◉Production-style systems and business impact ↗Cloud / Lakehouse / Orchestration +Open to Data Engineering opportunities
Bodin Krongchon working at a laptop in a data engineering workspace
01

Problem Solver

Start with the business problem and the data pain behind it.

02

Builder

Create reusable systems that can be operated and extended.

03

Owner

Trace failures through evidence and verify recovery.

04

Learner

Keep future learning separate from verified capability.

07 / CREDENTIALS

Credentials that can be opened and verified

Only credentials with a confirmed public link are shown here.

CV

Thai & English Resumes

View my Thai and English resumes.

08 / CONTACT

Let's build reliable
data systems.

For Data Engineering opportunities, portfolio review, or technical conversations, use the channels below.

Send Message→