Data Engineer
Job description
About the role
This role builds and maintains the data infrastructure that powers a global marketplace. It enables shopping behavior analytics at scale, connecting hundreds of millions of shoppers with retailers.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
These pipelines fuel analytics that guide business decisions across multiple markets.
Reliable foundations for analytics and reporting teams are established through this work.
Retail environment requirements are addressed through these collaborative solutions.
Streamlined data workflows and improved deployment reliability result from these pipelines.
Scalability and fit for large-scale data challenges guide these evaluations.
Tools and contributions are developed for internal use and open source projects where applicable. Broader data engineering communities benefit from these developments.
Requirements
A Bachelor's degree in Computer Science, Engineering, or an equivalent field is required for this position. Foundational knowledge for the role is provided by formal education.
A minimum of 2+ years of work experience in building data pipelines that ingest and transform large datasets is required. Hands-on implementation in similar environments is expected from candidates.
A minimum of 2+ years of work experience with big data technologies such as Spark, Kafka, and Airflow is required. Deployment of these technologies is expected to be proven.
Experience in one or more general purpose programming languages, preferably Scala or Python, is required. Code quality and maintainability are emphasized in daily tasks.
A track record of shipping quality code and delivering high quality features is required. Reliability and execution are key measures of success.
Passion for data and driving a data driven culture within the organization is required. Collaboration and insight generation are central to this position.
Practical notes
The role is based in Italy and offers a hybrid model with flexibility across Europe. Full remote work is available depending on operational needs.
The team size is approximately 450 people from 30 different nationalities, operating under fast-moving, growth-oriented conditions.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Data engineering roles focus on reliable data movement and infrastructure that supports analytics. Professionals in this field often work with distributed systems and large datasets.
Common tools include stream processing frameworks, message queues, and cloud platforms. These technologies enable real-time and batch data workflows.
Collaboration across data science and analysis teams is typical to ensure insights align with business goals. Clear documentation and transparency support effective data sharing.
Continuous integration and delivery practices help maintain code quality and accelerate feature releases in production environments.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.