Data Engineer

Work somewhere with the creativity of a scaleup and expertise of an enterprise.

We are looking for a savvy Data Engineer to join our growing team of analytics experts in Milan.

You’d be responsible for expanding and optimizing our data and data pipeline architecture, as well as for improving data flow and collection for cross-functional teams. You’d support our AI Research Engineer, Machine Learning Engineers and Backend Developers on data initiatives, ensuring consistent data delivery architecture throughout ongoing projects.

Should you be successful, you would:

• Create and maintain optimal data pipeline architectures
• Assemble large, complex data sets that meet functional and business requirements
• Identify, design, and implement improvements to internal practices: automating manual processes, optimizing data delivery
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of sources

Requirements

What You Have

• At least two years’ proven experience in a data engineer role
• A degree in Computer Science, Applied Math, Informatics, Information Systems or similar
• Experience building processes supporting data transformation, data structure, metadata, dependency and workload management
• Advanced working knowledge and experience with relational databases, query authoring as well as working familiarity with a variety of databases, such as SQL and NoSQL
• Experience building and optimizing data pipelines, architectures and data sets
• Experience with Python or Scala.
• Experience with distributed computing.
• Hands-on experience with object-oriented design, coding, and testing patterns.
• Knowledge of pipeline and workflow management tools (Airflow, Argo Workflows, …)
• Experience with CI/CD tools (Jenkins, Travis, Argo CD, Terraform...)
• Good understanding of Cloud and data models (data warehouse, data lake) and current engineering practices such as distributed architecture.
• Experience with MLOps is a plus.
• Experience with HPC is a plus.

Who You Are

• An enthusiastic individual, excited by the prospect of optimizing or even contributing to designing our company’s data architecture
• An analytical thinker with a passion for data
• Proactive, comfortable working alone or as part of a team
• Super organized
• Accurate, with strong attention to detail
• Familiar with Agile development
• Fluent in English

Benefits

Working at iGenius

With a growing team in four offices — Milan, NYC, London and Lausanne — iGenius is a scaleup that thinks like an enterprise, where talented innovators can thrive and people come first. That’s not all.

Perks

• Learning Fridays. If our team members know more, so do we. That’s why we give everyone a training budget that they can spend on books, online courses or other training materials
• Smart Working. Trains can be a drag, so we let our team members work from home when they can
• Salary is based on experience and topped up with other bonuses.

About iGenius

Established in 2016 by Uljan Sharka, iGenius is an AI company on a mission to humanize and democratize business data with Crystal, our proprietary GPT for numbers platform.

Unlike traditional BI tools, Crystal takes a human-first approach that enables business users to have high value conversations with data, all in an easy to use intuitive platform.

By combining data science, machine learning and conversational AI, Crystal becomes a private AI brain for business data, providing organizations with instant, safe and relevant data insights leading to faster and better decision making and business growth.

iGenius announced the partnership with Cineca to create the first Italian Foundational Large Language Model to help companies and Public Administration leverage the benefits of Generative Artificial Intelligence. 

With a strong global presence, iGenius operates in 4 countries with offices in New York, San José, Lausanne, London, and Milan, the company's headquarters.

The team counts +100 talented international professionals, highly skilled in AI, machine learning, data science, and software engineering.

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