Data Engineer (100% remote)

At Tether, we're committed to making advanced AI technologies more accessible. Thanks to its investment in AI infrastructure, starting from Northern Data, Tether is now in a prime position to tackle ambitious AI projects. Our goal is to build the next generation of AI models, leading innovation in AI, through an accessible, transparent and privacy preserving approach.The role involves building AI solutions across the spectrum from large-scale models designed for advanced applications to smaller, highly performant models tailored for efficiency on edge devices such as mobile phones and laptops.Our dynamic team operates entirely remotely, uniting talent from every corner of the globe. Our journey has been marked by rapid growth and efficient operations, firmly establishing us as pioneers within the industry. Join us in building AI models and solutions that not only compete with but exceed the capabilities of current leaders, driving both technological advancement and broad accessibility.

What You'll Do:

• Design, build, and maintain efficient, reusable, and reliable Python code to construct data pipelines.
• Work closely with data scientists and analysts to understand and fulfill their data requirements.
• Ensure the performance, quality, and responsiveness of data pipelines.
• Identify bottlenecks and bugs, and devise solutions to these problems.
• Maintain code quality, organization, and automatization.
• Develop and implement data collection systems and other strategies that optimize statistical efficiency and data quality.
• Collaborate with teams to integrate systems.

Job requirements

• 4+ years of experience in a Data Engineer role.
• Strong programming skills in Python.
• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases.
• Strong analytic skills related to working with unstructured datasets.
• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.

Preferred Qualifications:

• Degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
• Experience with machine learning algorithms and libraries.
• Familiarity with data visualization tools.