I'm a passionate Data Engineer with strong expertise in data engineering, cloud infrastructure, and DevOps automation, dedicated to designing scalable, secure, and high-performance data solutions that turn complex datasets into actionable insights. My experience spans building enterprise-grade data platforms, automating workflows, and deploying intelligent systems across diverse industries including automotive, finance, and IT services.

I specialize in building end-to-end ETL pipelines using Python, Azure Data Factory, IBM DataStage, and SQL Server to integrate ERP, IoT, and third-party data into unified, analytics-ready repositories. My work includes designing data lakehouse architectures with Databricks (PySpark) and Microsoft Fabric (OneLake, Dataflows, Lakehouse), developing predictive analytics dashboards in Power BI, and implementing real-time data ingestion pipelines from IoT telemetry.

With a strong focus on cloud engineering, I have optimized Azure Blob Storage, AWS S3, and GCP IAM environments for performance, governance, and compliance. I manage DevOps pipelines using Git and GitHub, deploy Kubernetes clusters, and automate workflows with Power Automate, RPA, and CI/CD pipelines. I am also experienced in Linux administration, IAM policy governance, SIEM log analysis, and integrating MFA/SSO solutions to strengthen cloud security.

Beyond core engineering, I have designed and implemented automated testing frameworks for ERP systems, developed AI-driven solutions such as NLP-based voice assistants, and created anomaly detection dashboards using Elasticsearch and Grafana. My solutions have improved audit readiness, accelerated decision-making, and reduced manual processes across multiple enterprise platforms.