Snowflake/ Databricks Data Engineer

Accountabilities: Data Pipeline Development: Design, build, and maintain scalable data pipelines using Snowflake or Databricks, Microsoft Azure, and Azure Service Bus to support enterprise-wide data initiatives. Data Integration: Lead integration efforts by extracting data from external sources and ingesting it into Snowflake, ensuring reliable and efficient data flows. Data Modeling & Transformation: Develop and maintain data models and transformation processes that convert raw data into trusted, authoritative sources. Business Enablement: Collaborate with stakeholders to create reliable data assets that support data commercialization, business intelligence, and strategic decision-making. Agile Collaboration: Participate in sprint planning, backlog grooming, daily stand-ups, and other Agile activities using tools such as Jira, ServiceNow, and Azure DevOps. Cloud Modernization: Support the transition from legacy data integration tools, such as SnowMirror, to modern cloud-native solutions. Self-Service Analytics: Enable accessible and reliable analytics through platforms such as Power BI and Sigma, promoting data integrity and usability. Automation & Innovation: Contribute to automation, scripting, and AI-driven data initiatives while continuously improving data engineering processes. Requirements: Professional Experience: At least 5 years of experience in data engineering, data architecture, or a related technical field. Cloud Data Platforms: Strong hands-on experience with Snowflake, Databricks, and Microsoft Azure in enterprise environments. Messaging & Integration: Practical experience with Azure Service Bus and enterprise data integration processes. Data Engineering Expertise: Proven ability to design and develop scalable data pipelines, data models, and data architectures. Data Transformation: Experience extracting data from external sources, managing ingestion workflows, and transforming raw data into trusted datasets. Agile Methodologies: Familiarity with Agile development practices and tools such as Jira, ServiceNow, and Azure DevOps. Analytics Platforms: Working knowledge of Power BI, Sigma, and self-service analytics principles. Programming & Automation: Exposure to Python, with an interest in contributing to scripting, automation, and AI-enabled data initiatives. Problem-Solving: Strong analytical skills and the ability to address complex data integration and architecture challenges. Collaboration: Effective communication skills and the ability to work closely with technical teams and business stakeholders in a fast-paced environment. Adaptability: Ability to contribute to cloud modernization initiatives and work with evolving data technologies and enterprise requirements. Benefits: Employment Type: Full-time position. Work Arrangement: Remote opportunity, open to candidates across the United States. Professional Exposure: Opportunity to work with modern cloud data platforms and enterprise-scale data engineering initiatives. Innovation: Involvement in data commercialization, self-service analytics, automation, and AI-driven data projects.