Mohammed Khan

About Mohammed Khan

As a Data Engineer with nearly 4 years of hands-on experience, I specialize in building scalable, automated data pipelines and AI-driven workflows across domains like payroll, finance, manufacturing, and customer experience. My work has consistently delivered measurable results—reducing manual effort by up to 95%, increasing data accuracy by over 40%, and enabling real-time analytics for better decision-making.I bring deep technical expertise in Python, PySpark, SQL, and orchestration tools like Apache Airflow and AWS Glue, along with a strong command of cloud platforms including AWS, GCP, and Azure. From implementing ML pipelines on Vertex AI to integrating OpenAI and OCR for ticket automation, I’ve engineered solutions that solve real business problems and improve operational efficiency.Whether it’s automating tax data validation across 50+ jurisdictions or optimizing defect classification models in semiconductor manufacturing, I thrive at the intersection of data, automation, and impact. I also prioritize governance and quality—embedding tools like Great Expectations, DataDog, and Azure Purview into every solution I deliver.I’m always looking to collaborate on high-impact projects where data engineering drives real-world transformation.

Experience

1. Data Engineer

   Gusto

   Aug 2024 — Present

   Project: AI-Powered Payroll Automation & Tax IntelligenceTech Stack: AWS Glue, Snowflake, Apache Airflow, PySpark, OpenAI, OCR, Great Expectations, DataDog, CloudWatch, Terraform, GitHub Actions, Python, SQL, REST APIs• Prepared payroll data ingestion for 300K+ clients using Airflow, AWS Glue, and Snowflake, reducing audit report generation time by 95% and replacing manual, spreadsheet-based workflows.• Integrated OpenAI LLMs and OCR pipelines to parse tax notices and auto-summarize support tickets, decreasing manual case handling by 70% and cutting response time from 15 minutes to under 1 minute.• Improved reporting accuracy by 45% by embedding Great Expectations into PySpark pipelines to validate tax and wage data across 50+ jurisdictions, flagging mismatches before downstream processing.• Cut incident resolution time by 40% with real-time alerts and observability via DataDog and CloudWatch, and managed infrastructure and deployments using Terraform and GitHub Actions.• Delivered real-time AI-generated compliance summaries through secure APIs and dashboards, increasing operational insight for 100+ internal users and contributing to a 12-point NPS gain during peak filing season.

Education

• University of Maryland Baltimore County

  Master's degree

  2022 — 2024