Data Engineer

Description

The team is the central engineering group behind a customer data platform (CDP) that serves millions of customers across Europe. You will help design and run the shared backend that supports campaign and customer analytics, the e-commerce platform, and partner and channel management.

You will work directly on shared components that business domains rely on, keeping their pipelines reliable as they handle very large daily data volumes. The work is part of the company's AI and data-driven shift.

The company is a Fortune 500 firm with 22,000 employees, operating in over 100 countries and known as the world's biggest technology distributor.

Current focus areas include marketing campaign operations with real-time automation and A/B testing across 140K+ monthly tasks; customer analytics such as churn prediction and real-time scoring; e-commerce intelligence with ML recommendations and personalization; partner and channel management with automated targeting, vendor rebate processing and multi-tier relationship systems; and data infrastructure on Snowflake, Databricks and Azure, covering 200+ schemas with 95%+ uptime targets.

Responsibilities

• Build AI-powered data platforms with MLOps frameworks, automated monitoring and pipelines that repair themselves and cut manual work.
• Add new data sources to enrich the datasets behind analytics, and help pass data science outputs on to business channels.
• Build enterprise-scale pipelines handling 100K+ monthly tasks across real-time campaigns, customer analytics and e-commerce recommendations.
• Automate work with AI coding assistants and agentic frameworks to remove repetitive tasks and raise engineering output.
• Build frameworks that manage AI agents, which turn integrated datasets into insight for end users.
• Apply DevOps practices to improve the environment and keep 200+ database schemas at 99% uptime.

Requirements

• 3+ years of applied data engineering experience.
• Strong SQL, including complex joins, subqueries, and knowledge of ETL/ELT and data modelling.
• Python skills for data wrangling, object-oriented programming and automating work on large datasets.
• Experience with cloud data platforms. Knowledge of the Azure stack is a plus.
• Working knowledge of Databricks, including Unity Catalog, workflows and compute management.
• Proficiency in Apache Spark and PySpark: distributed processing, DataFrames, Spark SQL and use within Databricks.
• DevOps practice covering CI/CD pipelines, automated testing, Git version control and deployment strategies.
• Ability to turn business needs into technical specifications and present insights to stakeholders.
• A systematic approach to resolving incidents, with experience integrating across platforms.
• A BSc, MSc or PhD in computer science, AI or a related STEM field is a plus.

Conditions

Hybrid setup: 3 days in the office and 2 days remote.

Stable environment with startup-style dynamics and an agile, experimental way of working.

The company values diverse voices and supports equal growth opportunities for everyone.

Benefits

• Elective benefits tailored to your country.
• Career growth through leadership and professional development programs, plus many on-demand courses.
• Well-being support through seminars, events and a global Life Empowerment Assistance Program covering financial, physical and mental health.
• An onboarding program that connects you with new colleagues within your first 30 days.
• Peer-led inclusive communities, business resource groups, local volunteering and environmental and social initiatives.