Snowflake Data Engineer

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Snowflake Data Engineer to join our innovative team. Snowflake Data Engineer | About You As a Snowflake Data Engineer, you are responsible for transforming raw data into trusted, authoritative sources that drive business decisions. You thrive in a fast-paced, agile environment and enjoy solving complex data integration challenges. You’re passionate about data commercialization, AI-driven insights, and enabling self-service analytics across the organization. Your ability to collaborate across teams and leverage modern cloud technologies like Azure and Snowflake makes you a key player in our data strategy. Snowflake Data Engineer | Day-to-Day

• Design and maintain scalable data pipelines using Snowflake, Azure, and Azure Service Bus to support enterprise-wide initiatives.
• Lead data integration efforts, extracting data from external sources and landing it in Snowflake, including data modeling and transformation.
• Collaborate with stakeholders to develop authoritative data sources that support data commercialization and strategic decision-making.
• Use Agile tools like Jira, ServiceNow, and Azure DevOps for sprint planning, backlog grooming, and daily stand-ups.
• Support modernization efforts by transitioning from legacy tools (e.g., SnowMirror) to cloud-native solutions.
• Enable self-service analytics through platforms like Power BI and Sigma, ensuring data integrity and accessibility.

Snowflake Data Engineer | Skills & Experience

• 5+ years of experience in data engineering, data architecture, or a related technical field.
• Strong hands-on experience with Snowflake, Azure, and Azure Service Bus in enterprise environments.
• Proven ability in data modeling, pipeline development, and scalable data architecture.
• Familiarity with Agile methodologies and tools such as Jira, ServiceNow, and Azure DevOps.
• Working knowledge of Power BI, Sigma, and principles of self-service analytics.
• Exposure to Python and interest in collaborating on automation, scripting, and AI-driven data initiatives.