Roshuddin Roshan Khan

About Roshuddin Roshan Khan

Azure Data Engineer with strong hands-on experience building scalable, high-performance cloud data platforms on Azure. I specialize in designing end-to-end data pipelines using Azure Databricks and Azure Data Factory, delivering reliable, analytics-ready datasets for business intelligence and advanced analytics.In my current role at LTIMindtree, I work extensively on Databricks-based data engineering, including PySpark transformations, medallion architectures (bronze/silver/gold), incremental processing, and performance optimization. I focus on writing efficient Spark code, tuning jobs for cost and scalability, and ensuring data pipelines are production-grade and resilient.I’m passionate about modern data engineering, cloud-native architectures, and Databricks ecosystems, and I continuously upskill to build data platforms that scale with business needs and unlock real business value.Core skills: Azure Databricks | PySpark | Azure Data Factory | Python | SQL | Cloud Data Engineering

Experience

1. Senior Data Engineer

   LTIMindtree

   Aug 2025 — Present · Mumbai, IN

   Designed and implemented end-to-end data pipelines for a large-scale supply chain platform to ingest SAP S/4HANA,FourKites, and third-party logistics data, enabling near real-time visibility into shipment status and delays.– Built scalable PySpark-based data processing workflows on Azure Databricks using a medallion architecture (bronze, silver,gold) to standardize raw logistics data and deliver analytics-ready datasets for Power BI and downstream analytics.– Developed and orchestrated batch and near real-time workflows using Azure Data Factory and Databricks Jobs, handlingincremental loads, schema evolution, and late-arriving data across heterogeneous data sources.– Implemented Databricks cost-optimization measures, achieving 25-30% compute cost reduction through clusterright-sizing, auto-scaling, job clusters, and Spark performance tuning.– Improved pipeline reliability and data freshness by handling late-arriving data, schema drift, and idempotent processingpatterns in distributed Spark workloads.– Designed analytics-ready data models aligned with supply chain KPIs, improving query performance and ensuring consistentmetrics across operational and analytical reporting layers.– Collaborated with supply chain stakeholders, data scientists, and analytics teams to translate ambiguous businessrequirements into scalable data transformations and reliable analytics-ready datasets.

Education

• M.H. Saboo Siddik College Of Engineering

  Bachelor's, Electronics and Telecommunications

  2016 — 2020