<p>We are seeking an experienced <strong>Data Engineer</strong> to architect, build, and optimize scalable data pipelines and cloud-based data platforms that support advanced analytics, reporting, and business intelligence initiatives.</p><p><strong>Responsibilities</strong></p><ul><li>Design, develop, and maintain robust ETL/ELT pipelines for ingesting, transforming, and loading structured and semi-structured data.</li><li>Build and manage scalable data pipelines using <strong>Python</strong> and <strong>SQL</strong> across cloud and warehouse environments.</li><li>Optimize data storage, query performance, and overall efficiency within modern data warehouse platforms.</li><li>Develop and maintain data models that support downstream analytics, reporting, and operational use cases.</li><li>Collaborate with business stakeholders, analysts, and engineering teams to define data requirements and translate them into technical solutions.</li><li>Implement and support data quality checks, governance standards, and monitoring processes to ensure data reliability and integrity.</li><li>Troubleshoot pipeline failures, performance bottlenecks, and data consistency issues across the data ecosystem.</li><li>Contribute to best practices in data architecture, orchestration, documentation, and deployment.</li></ul><p><br></p>

<ul><li>4+ years of hands-on experience in data engineering or a related field.</li><li>Strong proficiency in <strong>Python</strong> and advanced <strong>SQL</strong> for data transformation, automation, and performance tuning.</li><li>Experience with modern cloud data tools and platforms such as <strong>Azure Data Factory</strong>, <strong>Snowflake</strong>, <strong>Databricks</strong>, or <strong>AWS</strong>-based data services.</li><li>Solid understanding of <strong>data modeling</strong>, <strong>data warehousing</strong>, and ETL/ELT design principles.</li><li>Experience working with large-scale datasets and building reliable, production-grade data solutions.</li><li>Familiarity with data governance, data validation, and pipeline orchestration best practices.</li></ul><p><br></p>

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