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Designs and oversees the architecture of scalable, high-performance big data platforms that support advanced analytics, data science, and business intelligence initiatives.
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Leads the integration of structured and unstructured data from multiple sources into enterprise data lakes or warehouses using distributed computing frameworks (e.g., Hadoop, Spark, Kafka).
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Defines data management strategies, including storage, security, data lifecycle, and governance, ensuring compliance with organizational and regulatory standards.
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Collaborates with data engineers, data scientists, and business stakeholders to understand technical and functional requirements and translate them into architectural blueprints.
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Selects appropriate big data technologies and tools, balancing performance, scalability, and cost-effectiveness based on workload needs.
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Develops and maintains reference architectures, best practices, and design patterns for data ingestion, transformation, storage, and processing.
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Leads data platform modernization efforts, including migrations to cloud-based ecosystems such as AWS, Azure, or Google Cloud Platform.
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Establishes and enforces architectural standards, ensuring reusability, scalability, and robustness across all data engineering pipelines.
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Mentors junior architects and engineers, promoting knowledge sharing, technical growth, and consistent architectural practices.
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Stays informed on emerging trends in big data, AI/ML infrastructure, and evolving cloud-native architectures to continuously improve system capabilities.
