Data Engineer

Andersen is a global technology provider specializing in data analytics and AI-powered solutions that help organizations better understand customers, optimize marketing, and make data-driven decisions. Andersen is a global software development company with over 18 years of experience supporting businesses worldwide in their digital transformation. Headquartered in Poland, Andersen operates development centers across Germany, the USA, the UK, Lithuania, Hungary, Kazakhstan, Georgia, Azerbaijan, Cyprus, and the UAE. The company offers end-to-end IT services including custom software development, API integration, system architecture, legacy modernization, and long-term support. Andersen has a team of over 3,700 experts across more than 16 global locations and has delivered over 1,000 projects across industries such as Healthcare, FinTech, Logistics, Automotive, Manufacturing, E-commerce, Multimedia & Entertainment.

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent.
• 5+ years of experience as a Data Engineer.
• Hands-on production experience on AWS or Databricks.
• Strong proficiency in Python and PySpark at production quality.
• Experience designing and developing API-first services.
• Experience with Apache Airflow in production.
• Hands-on experience with Kubernetes infrastructure management.
• Strong understanding of distributed data processing and workflow orchestration.
• English language proficiency from Upper-Intermediate and above.

Nice to have:

• Background in ACR or television measurement systems.
• Experience with identity-resolution frameworks.

• Analyzing and resolving pipeline failures, triaging data integrity issues, and applying targeted code corrections.
• Addressing operational tickets during designated hours and responding to production incidents as assigned.
• Delivering validated pull requests with automated testing consistent with company standards and providing concise technical summaries for each ticket.
• Transitioning legacy processes to Kubernetes-native operators and maintaining Airflow compatibility.
• Refactoring code in line with the customer’s engineering standards.
• Supporting cross-functional integration requirements in collaboration with internal teams.