Mohammad Faizal

About Mohammad Faizal

Senior Data Engineer with 5+ years of experience designing and building scalable data platforms across banking, healthcare, and retail domains. Specialized in developing batch and near real-time data pipelines using Azure, AWS, and modern data stack tools including Databricks, Snowflake, dbt, and Airflow. Strong expertise in PySpark, advanced SQL, and dimensional data modeling to deliver analytics-ready datasets and enterprise KPI frameworks. Proven track record of processing large-scale datasets (500GB–2TB+), improving pipeline performance, and implementing robust data quality, monitoring, and governance frameworks. Experienced in building CI/CD pipelines using Azure DevOps and GitHub Actions for reliable and automated deployments. Collaborate closely with cross-functional teams including business, risk, and finance to deliver data solutions that drive executive decision-making. Passionate about performance optimization, scalable architecture, and mentoring teams.

Experience

1. Senior Data Engineer Analytics & Data Platform

   Capital One

   Feb 2025 — Present · US

   Designed and maintained Azure-based batch and near real-time data pipelines processing 1TB+ monthly portfolio and risk datasets for credit exposure monitoring• Built scalable PySpark transformations in Azure Databricks improving processing efficiency by 35% over legacy SQL workflows• Developed analytics-ready dimensional data models in Azure SQL and Snowflake standardizing 30+ KPIs across finance and risk teams• Implemented modular ELT transformations using dbt with automated testing and version control to reduce deployment defects• Orchestrated pipelines using Azure Data Factory triggers, dependency chains, and incremental load strategies improving pipeline reliability by 20%• Implemented CI/CD pipelines using Azure DevOps and Git branching strategies ensuring consistent and error-free deployments• Introduced automated data quality validation, reconciliation checks, and anomaly detection reducing reporting inaccuracies by 30%• Engineered curated datasets supporting downstream ML-based risk scoring models improving feature stability and consistency• Mentored junior engineers on Spark optimization, SQL tuning, and dimensional modeling best practices• Documented data lineage, KPI definitions, and governance standards supporting audit and regulatory reporting requirements

Education

• Southeast Missouri State University

  Master's degree, Applied Computer Science

• Badruka College Of Commerce

  Bachelor of Commerce - BCom, Computers