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Architected and delivered scalable data pipelines using Python (PySpark, FastAPI), Azure Databricks, and RESTful APIs for enterprise analytics
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Built and optimized ETL/ELT pipelines leveraging SQLAlchemy ORM, PostgreSQL, and Azure Synapse, reducing query execution time and improving data quality
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Developed microservices and REST APIs with FastAPI and Flask, integrating with PostgreSQL and managed via Docker and Kubernetes for high availability
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Orchestrated batch and streaming workflows using Apache Airflow, Azure Functions, and event-driven triggers, automating over 150 production jobs
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Implemented CI/CD pipelines with GitHub Actions and Azure DevOps for automated testing, deployment, and version control of Python-based workloads
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Optimized PostgreSQL databases through advanced indexing, partitioning, and query tuning for improved reporting and analytics performance
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Designed metadata repositories and configuration management using Cosmos DB, supporting audit logging and workflow execution tracking
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Collaborated with cross-functional teams to translate requirements into technical solutions, participating in design reviews and Agile delivery
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Applied data governance and MDM practices, ensuring data lineage, quality, and compliance across distributed systems
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Secured enterprise data with Azure AD, Key Vault, RBAC, and managed identities, aligning with regulatory standards
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Provisioned infrastructure using Terraform and ARM templates for scalable, policy-driven deployments
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Integrated data from PostgreSQL, REST APIs, and cloud storage, supporting robust analytics and reporting pipelines