Snowflake/ Databricks Data Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Snowflake / Databricks Data Engineer based in the United States.

This is an opportunity for an experienced Data Engineer to help build and maintain modern, scalable data solutions that turn raw information into trusted, business-ready assets. Working within a fast-paced, agile environment, you will contribute to enterprise-wide data integration and cloud modernization initiatives. The role focuses on leveraging Snowflake, Databricks, and Microsoft Azure to develop reliable pipelines, robust data models, and authoritative data sources. You will collaborate with technical teams and business stakeholders to support data commercialization, strategic decision-making, and AI-driven insights. Your work will also help expand self-service analytics capabilities while improving data accessibility, integrity, and overall value across the organization.

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.