● OPEN TO WORK

Muhammad Ridho Heranof

|

Specialized in building robust end-to-end data pipelines, optimizing database performance, and designing scalable cloud architectures. I turn raw data into actionable insights using modern stack technologies.

Download Resume

Tech Stack

PROCESSING

Python

Spark

dbt

Databricks

WAREHOUSE

Postgres

BigQuery

ClickHouse

MongoDB

INFRASTRUCTURE

Docker

Airflow

AWS

Git

Experience

DEC 2023 — PRESENT

Database Engineer

PT Neural Technologies Indonesia

Optimized PostgreSQL database performance, increasing query speed by 5-10x through indexing strategies.
Managed daily ETL processes supporting 40K+ rows and collaborated with Data Science teams for ML pipelines.
Configured and secured database parameters to ensure high availability and compliance.
Implemented complex SQL logic using Spark RDDs for efficient extraction and transformation.

APR 2023 — NOV 2023

Data Engineer

PT Pusat Inovasi Nusantara

Built an end-to-end data pipeline using Docker, Airflow, GCP, Spark, and Kafka within 1 month.
Developed real-time fraud mitigation dashboards using Looker Studio.
Optimized ETL/ELT processes in Airflow by splitting tasks to reduce execution time.
Ingested streaming data into data lake using Apache Kafka producers and consumers.

JUN 2022 — JAN 2023

IT Consultant Intern

Zephrum Consultant Limited

Designed scalable data ingestion pipelines using Python and SQL for financial clients.
Ensured seamless system integration within Linux-based deployment environments.
Collaborated with cross-functional teams to deliver solutions meeting business needs.
Maintained rigorous security and performance standards for client data systems.

Featured Projects

A deep dive into the systems I've architected. Handling scale, speed, and reliability.

SYSTEM_ARCHITECTURE_V1.0

Fraud Detection Pipeline

Kafka • Spark • BigQuery

Latency < 200ms 99.9% Uptime

def process_stream(data):
df = spark.readStream(data)
return df.writeStream.start()

Data Ingestion

Handling thousands of transactions per second using Apache Kafka as a real-time message broker.

Processing

Raw data is processed using Apache Spark. Implemented algorithms to detect anomalies based on historical patterns.

Storage & Viz

Clean data is loaded into Google BigQuery. Real-time monitoring dashboards built with Looker Studio.

GITHUB REPO

Other Deployments

Enterprise Data Warehouse

Arsitektur data warehouse terpusat untuk mendukung analytics skala besar menggunakan BigQuery dan dbt untuk transformasi data.

BigQuery SQL

Sales Forecasting Model

Prediksi penjualan harian menggunakan Python (Scikit-Learn) yang terintegrasi langsung dengan dashboard operasional.

Python ML