<p>We are looking for a senior Data Engineer. This position combines architectural leadership with hands-on development across a modern cloud data ecosystem, with a strong focus on building dependable, scalable, and efficient data solutions. The role will help shape engineering standards, strengthen platform governance, and support high-quality data delivery for enterprise analytics and emerging AI-driven use cases.</p><p><br></p><p>Responsibilities:</p><p>• Provide technical direction to engineering teams and guide the successful execution of complex data platform initiatives.</p><p>• Create and enhance robust data pipelines and reusable data products that support scalable analytics and reporting needs.</p><p>• Develop and refine cloud-based data solutions using Snowflake, dbt, Fivetran, and Azure services.</p><p>• Build automation-first, metadata-driven processing frameworks to improve consistency, maintainability, and operational efficiency.</p><p>• Design curated data structures using established modeling approaches such as Data Vault, Kimball, and dimensional modeling.</p><p>• Improve platform performance through tuning, cost management, and scalability planning across data workloads.</p><p>• Establish and reinforce best practices for data quality, governance, lineage tracking, observability, and CI/CD delivery.</p><p>• Support Azure-based integration, storage, compute, and DevOps capabilities required for reliable end-to-end data operations.</p><p>• Enable data foundations that can support advanced analytics, AI/ML initiatives, and modern analytical architectures.</p>

• 12+ years of experience in data engineering and enterprise data platform design.<br>• Advanced hands-on expertise with Snowflake, dbt, and Fivetran in production environments.<br>• Strong background working with Microsoft Azure services and DevOps or CI/CD practices.<br>• Demonstrated experience building large-scale data models, frameworks, and architecture patterns for enterprise use.<br>• Proven ability to lead technical teams, conduct code reviews, and define engineering standards.<br>• Solid knowledge of data governance, data quality controls, and operational monitoring practices.<br>• Experience with data modeling methods such as Data Vault, Kimball, and dimensional modeling.<br>• Familiarity with data platforms that support AI/ML, including modern analytics architectures, is preferred.

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