Senior Snowflake Data Engineer

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT. Z nami znajdziesz konkurencyjne oferty zatrudnienia oraz ciekawe projekty IT skierowane zarówno do ekspertów z wieloletnim doświadczeniem, jak i osób, które dopiero zaczynają swoją przygodę w branży IT.

• Proven experience working with Snowflake as a data engineering platform.
• Strong knowledge of data engineering concepts, including ETL/ELT processes, data integration, and pipeline development.
• Hands-on experience with data modeling techniques and designing scalable data solutions.
• Experience designing Snowflake architectures, including security, performance optimization, and platform governance.
• Strong understanding of cloud platforms, particularly Microsoft Azure.
• Experience implementing and managing security solutions across Snowflake and Azure environments.
• Familiarity with CI/CD practices and automation for data platform deployments.
• Ability to translate business requirements into technical designs and deliver practical data solutions.
• Strong analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.

• Design, develop, and maintain scalable Snowflake data platform architectures, including data models, security frameworks, data processing pipelines, and CI/CD processes.
• Define and implement Snowflake solutions aligned with business and technical requirements.
• Evaluate and select appropriate Snowflake components and features to meet platform scalability, performance, security, and operational needs.
• Design and implement robust ETL/ELT data pipelines to support data ingestion, transformation, and analytics use cases.
• Develop and optimize data models to ensure data quality, performance, and usability across business domains.
• Implement security best practices across Snowflake and Azure environments, including access controls, roles, permissions, and data protection mechanisms.
• Collaborate closely with business stakeholders to understand data needs, translate requirements into technical solutions, and deliver effective data engineering outcomes.
• Provide delivery oversight, ensuring projects are delivered on time, meet quality standards, and align with architectural guidelines.
• Support continuous improvement of the data platform through performance optimization, automation, and adoption of best practices.