Data Engineer

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.

Responsibilities:

• Provide technical direction to engineering teams and guide the successful execution of complex data platform initiatives.
• Create and enhance robust data pipelines and reusable data products that support scalable analytics and reporting needs.
• Develop and refine cloud-based data solutions using Snowflake, dbt, Fivetran, and Azure services.
• Build automation-first, metadata-driven processing frameworks to improve consistency, maintainability, and operational efficiency.
• Design curated data structures using established modeling approaches such as Data Vault, Kimball, and dimensional modeling.
• Improve platform performance through tuning, cost management, and scalability planning across data workloads.
• Establish and reinforce best practices for data quality, governance, lineage tracking, observability, and CI/CD delivery.
• Support Azure-based integration, storage, compute, and DevOps capabilities required for reliable end-to-end data operations.
• Enable data foundations that can support advanced analytics, AI/ML initiatives, and modern analytical architectures.
• 12+ years of experience in data engineering and enterprise data platform design.
• Advanced hands-on expertise with Snowflake, dbt, and Fivetran in production environments.
• Strong background working with Microsoft Azure services and DevOps or CI/CD practices.
• Demonstrated experience building large-scale data models, frameworks, and architecture patterns for enterprise use.
• Proven ability to lead technical teams, conduct code reviews, and define engineering standards.
• Solid knowledge of data governance, data quality controls, and operational monitoring practices.
• Experience with data modeling methods such as Data Vault, Kimball, and dimensional modeling.
• Familiarity with data platforms that support AI/ML, including modern analytics architectures, is preferred.