Snowflake/ Databricks Data Engineer

Jobgether; Oregon; Ibrido; USD 100.000 - 160.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote work

Full-time

Descrizione del lavoro

Jobgether is seeking a skilled Data Engineer in the United States to design and maintain scalable data pipelines using Snowflake, Databricks, and Azure. You will work with Azure Service Bus, Power BI, and Sigma to enable self-service analytics and robust data assets.

With 5+ years of experience, you will collaborate with technical teams and business stakeholders in a fast-paced environment, supporting cloud modernization and data commercialization initiatives.

Competenze

• 5+ years of data engineering, data architecture, or related field.
• Proven experience with Snowflake, Databricks and Azure in enterprise environments.
• Hands-on data integration and pipeline development with external sources.
• Experience with Agile methodologies and tools (Jira, ServiceNow, Azure DevOps).
• Knowledge of Power BI, Sigma, and self-service analytics.
• Programming exposure in Python and automation

Mansioni

• Design, build, and maintain scalable data pipelines using Snowflake or Databricks, Microsoft Azure, and Azure Service Bus to support enterprise-wide data initiatives.
• Lead integration efforts by extracting data from external sources and ingesting it into Snowflake, ensuring reliable data flows.
• Develop and maintain data models and transformation processes that convert raw data into trusted sources.
• Collaborate with stakeholders to create reliable data assets that support data commercialization, BI, and decision-making.
• Participate in sprint planning, backlog grooming, daily stand-ups, and other Agile activities using Jira, ServiceNow, and Azure DevOps.
• Support the transition from legacy data integration tools to modern cloud-native solutions.
• Enable accessible analytics through Power BI and Sigma, promoting data integrity and usability.
• Contribute to automation and AI-driven data initiatives while continuously improving data engineering processes.

Conoscenze

Snowflake

Databricks

Azure

Data pipelines

Data modeling

Power BI

Sigma

Python

Agile

Strumenti

Azure Service Bus

Jira

ServiceNow

Azure DevOps

Descrizione del lavoro

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.