Full-Stack Data Engineer

As a Full Stack Data Engineer, you lead the development and optimization of efficient end-to-end data pipelines and infrastructure, driving efficiency and innovation to meet complex business needs and analytics objectives. You collaborate in a cross-functional team environment, adapting to different challenges as needed.

You independently manage and complete complex data engineering initiatives with a strategic focus. You address and resolve technical challenges in scalable pipeline design and infrastructure optimization.

You are able to influence data architecture decisions and align with organizational priorities and best practices.

During your work, you collaborate in a cross-functional setting, ensuring alignment of technical solutions with business needs while fostering adaptability across disciplines.

You bring: 

• Bachelor’s or university degree, conceptual/strategic thinking level.
• Expertise in languages such as Python and SQL, with a focus on modern data tools such as dbt.
• Experience with cloud-based data warehouses such as Snowflake.
• Extensive knowledge of cloud platforms and experience designing cloud-native solutions.
• Strong proficiency in batch data processing frameworks and pipeline orchestration tools.
• Demonstrated ability to lead technical discussions and mentor less experienced engineers.
• Analytical mindset with the ability to balance technical depth and strategic priorities.

Delivering highly scalable and efficient data pipelines and architectures that align with business goals. You produce well-documented, maintainable solutions that enhance team productivity and system reliability. You establish and maintain data infrastructure that supports advanced analytics and operational scalability.

• Lead the design, implementation, and optimization of scalable ETL pipelines and data workflows.
• Develop and implement advanced data models to support analytics and machine learning.
• Collaborate with stakeholders in a cross-functional team to translate business requirements into robust data solutions, contributing beyond core responsibilities when needed.
• Implement best practices and drive improvements in data quality, governance, and security practices.
• Contribute to data architecture discussions, introducing innovative solutions and frameworks.
• Implement and advocate for best practices in cloud platforms and distributed computing frameworks.
• Set up monitoring and alerting for data quality in data pipelines.
• Handle the investigation, remediation, and reporting of data quality issues.