Wan Ahmad Fahim Munir

Data Engineer / Analytics Engineer

Building reliable data pipelines and analytics-ready datasets. Experienced in end-to-end data orchestration, transformation modeling, and cloud infrastructure.

Apache Airflow dbt SQL Python Docker AWS
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About Me

Building end-to-end data infrastructure

Fahim Munir

I'm a Data Engineer focused on building reliable, scalable data pipelines that transform raw data into analytics-ready datasets. My work spans the full data engineering lifecycle: from ingestion and orchestration to transformation modeling and serving data for business intelligence.

I specialize in modern data stack technologies including Apache Airflow for orchestration, dbt for transformation modeling, and cloud platforms like AWS for storage and compute. My experience includes building production pipelines processing millions of records daily with strong data quality guarantees.

Beyond infrastructure, I understand the analytics layer—working with business stakeholders to translate requirements into dimensional models, metrics, and dashboards. I'm equally comfortable writing SQL transformations, Python ETL scripts, or deploying containerized workflows with Docker.

Data Engineering

  • Pipeline Orchestration (Airflow)
  • Data Modeling (dbt)
  • ETL/ELT Design & Optimization
  • Data Quality & Testing

Cloud & Infrastructure

  • AWS (S3, Glue, Redshift)
  • Docker & Containerization
  • PostgreSQL, Snowflake, SQL Server
  • CI/CD & Git Workflows

Programming & Tools

  • Python (pandas, PySpark)
  • SQL (Advanced queries & optimization)
  • Apache Spark for distributed processing
  • REST API integration

Analytics & BI

  • Dimensional Modeling (Star/Snowflake)
  • Business Metrics Design
  • Tableau, Power BI
  • Machine Learning Pipelines

Experience

Building production data systems in healthcare and fintech

Analytics Engineering Intern

ResMed, Kuala Lumpur | May 2024 – Aug 2024

Built production-grade dbt transformation models converting Oracle ERP datasets into Snowflake analytics tables. Standardized SQL logic using Jinja templates, improving reusability and maintainability across finance, supply chain, and marketing analytics. Participated in Agile sprints with Git-based version control and CI/CD deployment cycles. Explored Dagster-based orchestration for pipeline automation. Supported cross-functional analytics teams with clean, well-documented dimensional models.

Analyst Programmer

InsiteMY Innovations, Kuala Lumpur | 2021 – 2022

Developed ETL pipelines using SSIS and SQL Server for major banking clients (BIMB, AmBank). Delivered critical reporting systems including Data Quality Framework (DQF), STATSMART, and Information Security System (ISS). Translated complex business requirements into efficient SQL queries and Java modules. Contributed to regulatory reporting for Bank Negara Malaysia, ensuring compliance and data accuracy in high-stakes financial environments.

Featured Projects

Production-grade data pipelines and analytics infrastructure

Primary Feature

Sekolah Mana? – Malaysian School Performance Data Pipeline

Problem: Malaysian parents and policymakers lack accessible, standardized data to compare school performance across states and regions. Raw government datasets are fragmented, inconsistent, and difficult to analyze at scale.

Architecture & Pipeline Flow

  • Bronze Layer: Raw data ingestion from Malaysian government APIs and CSV datasets
  • Silver Layer: Data cleaning, standardization, and schema validation
  • Gold Layer: Analytics-ready dimensional models for BI consumption
  • Orchestration: Apache Airflow DAGs scheduling daily incremental loads
10,000+
Schools Processed
5M+
Student Records
99.8%
Data Quality Score
Apache Airflow dbt PostgreSQL Python Docker AWS S3
View on GitHub
Secondary Feature

Weather & Flight Data Engineering Pipeline

Problem: Analyzing flight delays and cancellations requires integrating real-time weather data with historical flight records. Manual data collection is slow and error-prone, preventing timely operational insights.

Architecture & Pipeline Flow

  • Ingestion: API polling for real-time weather and flight status data
  • Processing: Apache Spark for distributed data transformation and enrichment
  • Storage: Partitioned Parquet files in AWS S3 for optimized query performance
  • Analytics: dbt models creating aggregated metrics for BI dashboards
500K+
Flight Records/Month
2.5M+
Weather Data Points
3x
Query Performance Gain
Apache Airflow Apache Spark dbt AWS S3 Python Docker
View on GitHub
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Get In Touch

Open to data engineering and analytics opportunities

Email wanahmadfahim828@gmail.com
Phone +6017-373 4879
Location Kuala Lumpur, Malaysia
GitHub github.com/ravager1204
LinkedIn Connect with me