Mohammad Maaz

About Mohammad Maaz

Data Engineer with 6+ years of experience in building large-scale data pipelines, ETL processes, and data warehouse solutions. Utilized technologies like Python, SQL, Spark, Snowflake, Dbt and Airflow to develop scalable big data solutions- Developed scalable big data solutions using modern technologies including Python, SQL, Spark, Snowflake, dbt, and Airflow- Redesigned Snowflake RBAC and virtual warehouses according to best practices, resulting in a 30% reduction in costs- Designed near real-time ELT pipelines to ingest staged ERP data into Snowflake using AWS S3, SQS, and MWAA (Airflow)- Automated database change management and infrastructure creation using Terraform scripts, SchemaChange, and GitHub Actions workflows- Managed Snowflake accounts, including RBAC, object tagging, and compute provisioning, and enabled secure SSO access via Okta- Automated Snowflake database management and AWS deployments by designing robust CI/CD pipelines using GitHub Actions and Terraform for Infrastructure as Code (IaC)- Led a team to develop an ELT data warehouse solution for a Hackathon and built KPI visualizations- Engineered resilient batch ingestion pipelines utilizing Change Data Capture (CDC) from external APIs, incorporating SHA-256 deduplication and PyMuPdf document classification for downstream LLM processing- Acted as Data Architect, directing teams to successfully migrate enterprise data pipelines across cloud platforms (Azure to AWS Databricks) and authoring strategic governance documentation.

Experience

1. Senior Lead Data Engineer

   v4c.ai

   Sep 2025 — Present

   Build batch data ingestion pipeline extracting gmail messages using historyId for CDC & attachments metadata from Gmail API with retry, error handling & auditing mechanism- Downloaded invoices pdf from attachments metadata into an external unity catalog volume- Implemented SHA-256 hashing on invoices for filtering out duplicate invoices based on file name, internal_date & attachment_id for downstream LLM extraction process- Utilized PyMuPdf library for document page count & text classification. LLM validated matched invoices transferred from Volumes to SFTP server using smbclient library- Export header & line level record from extracted invoices into Oracle interface tables- Deployed pipeline using Databricks Asset Bundle - Led a team of 2 developers to successfully architect and migrate production data pipelines from legacy Azure Databricks to AWS Databricks- Replicated and enhanced cluster policies in AWS Databricks, providing strategic recommendations for node configurations and access modes- Replaced legacy Hive Metastore dependencies by implementing Unity Catalog Volumes for secure and efficient intermediate data storage- Enhanced the existing codebase by designing robust error-handling mechanisms and integrating comprehensive audit tables- Engineered a custom, modular logging framework that pushes real-time failure alerts via Slack webhooks while maintaining logs in the audit table- Authored comprehensive architectural documentation outlining recommendations for platform setup, governance, and single vs. multiple workspace strategies.

Education

• CMR Institute Of Technology

  Bachelor of Engineering - BE, Electrical, Electronics and Communications Engineering

  2015 — 2019