Data Engineer

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Key Responsibilities Design, develop, and maintain scalable data pipelines and platform solutions across AWS cloud and hybrid/on-premises environments. Build and optimize ETL/ELT workflows using Python, PySpark, SQL, and cloud-native technologies. Develop and support data lakes, lakehouse architectures, metadata management, search platforms, and AI-enabled services. Implement and maintain CI/CD pipelines, automated testing frameworks, Infrastructure as Code (IaC), and deployment processes. Integrate and support AWS services and platform technologies, including OpenSearch, Amazon Bedrock, AgentCore, and Model Context Protocol (MCP). Monitor and optimize platform reliability, security, performance, observability, and cloud costs. Troubleshoot production issues, perform root cause analysis, and ensure platform stability and operational excellence. Collaborate with cross-functional teams to drive platform modernization, automation, and continuous improvement. Mandatory Requirements 3–5+ years of hands-on experience developing and supporting production-grade data pipelines using SQL, Python, and cloud technologies. Practical experience with AWS cloud services and/or hybrid cloud environments. Strong Linux/Unix administration, shell scripting, and production support capabilities. Solid understanding of data engineering principles, distributed processing, and enterprise platform operations. Technical Skills Data Engineering: ETL/ELT, data lakes, lakehouse architecture, distributed d

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