Md Imran khan

Data Engineer with ~4.6 years of experience designing, implementing, and maintaining enterprise ETL/ELT pipelines on AWS and Azure for data platforms serving FinTech and payments clients. Strong hands-on expertise in Apache Spark / PySpark, the Hadoop ecosystem, SQL, and orchestration tools (Apache Airflow, Azure Data Factory, AWS Glue) to deliver high-quality data on time and at scale. Processed 1M+ daily payment transactions, converted a manual workflow of 1.5 days into a fully automated pipeline, and consistently apply version control, code reviews, and data validation best practices.

Collaborative team player working with senior engineers, analysts, and stakeholders across local and global projects.

Data Analyst Engineer at EaseBuzz (2026-01 – Present)

• Designed and deployed 3 ingestion and 2 transformation ETL pipelines on AWS (S3 → Glue → Redshift) processing 1M+ daily payment transactions, achieving 100% automation of manual ingestion workflows.
• Automated a manual reconciliation process using AWS Glue and PySpark, replacing 1.5 days of manual effort with a fully automated, same-day pipeline and freeing up dedicated manpower.
• Built scalable PySpark-based AWS Glue jobs for high-volume financial data transformation, cutting pipeline execution time by ~35% through partition pruning and job optimization.
• Implemented data validation frameworks using AWS Glue Data Quality and custom checks, achieving 99%+ accuracy and 99.5% pipeline uptime with CloudWatch alerting.
• Developed event-driven triggers using AWS Lambda and S3 event notifications, ensuring timely, near-real-time data availability for payment processing workflows.
• Optimized SQL data models, stored procedures, and reporting layers in Redshift to reduce processing time on large data sets, supporting 10+ daily business reports and eliminating ~4 hours/day of manual effort.
• Implemented CloudWatch-based monitoring and alerting across 5+ Glue jobs, cutting pipeline-failure detection time by 50%.
• Partner with data engineers, analysts, and business stakeholders to deliver accurate, high-value data, following Git version control and peer code review practices.

Software Engineer at CoreCard Software India Pvt Ltd (2022-07 – 2025-12)

• Designed, implemented, and maintained enterprise ETL pipelines on Azure Data Factory (ADF) and AWS Glue for card processing and authorization event data, handling 500K+ daily records with zero data loss.
• Built and optimized PySpark jobs on Azure Databricks for large-scale financial data transformation, improving processing speed by 30% through cluster tuning, partitioning, and caching strategies.
• Built real-time fraud detection pipelines on Azure Databricks and AWS Glue processing 500K+ daily transactions using PySpark and Kafka-based event streaming, reducing fraud investigation turnaround time by 30%.
• Implemented Medallion Architecture (Bronze → Silver → Gold) on Azure Data Lake Gen2 using Delta Lake, enabling ACID transactions, data lineage tracking, schema evolution, and time-travel for audit and compliance.
• Architected 15+ analytical schemas and SQL views across 5+ source systems using Kimball dimensional modelling, tuning SQL to reduce processing time on large data sets and enable reliable BI reporting.
• Established data validation and governance practices including data contracts, SLA definitions, and lineage documentation across 5+ source systems, maintaining 99%+ data accuracy in production.
• Orchestrated multi-step ELT workflows using Apache Airflow on Azure with dependency management and alerting, automating routine data tasks and reducing processing errors by 25%.
• Automated deployment of ADF pipelines and AWS Glue jobs using Git-based version control and Azure DevOps CI/CD, reducing release cycle time by 40%.
• Migrated 10+ legacy on-premise ETL scripts to Azure Data Factory and AWS Glue, decommissioning manual workflows and cutting infrastructure/maintenance costs by 20%.
• Collaborated with senior engineers, analysts, and client-facing teams on data challenges for local and global projects; participated in code reviews and mentored 2 junior engineers on PySpark, Databricks, and AWS Glue best practices.

Bachelor of Engineering in Computer Science – Sagar Institute of Science Technology and Research (2019-08 – 2022-06)

Diploma in Computer Science – Govt College Bhaga (2014-07 – 2017-06)