Data Engineer

ABBYY ABBYY · Enterprise · Hungary · Business Operations

Data Engineer to build the data foundation for AI initiatives, design and maintain data pipelines for analytics, automation, and AI-driven experiences.

What you'd actually do

  1. Design and build scalable data pipelines that collect, transform, and deliver structured and unstructured data from internal and external sources.
  2. Partner with business and technology teams to define data requirements for AI use cases and operational workflows.
  3. Develop data models, integrations, and data quality controls to support analytics, automation, and model performance.
  4. Ensure data is available, reliable, secure, and properly governed across systems and environments.
  5. Work closely with AI engineers, product owners, and process teams to prepare data for experimentation and production use.

Skills

Required

  • Strong SQL and Python skills
  • experience building batch and/or streaming pipelines
  • Hands-on experience with data platforms, ETL/ELT tools, cloud environments, and data warehouses
  • Understanding of data quality, data governance, security, and access control principles
  • Ability to work cross-functionally and translate business needs into technical solutions
  • 5+ years designing and building end-to-to architectures
  • Experience with data modeling and database design

Nice to have

  • Experience supporting AI, machine learning, or automation initiatives
  • Strong financial acumen (ARR, margin, leverage, performance metrics)
  • Demonstrated ability to align strong functional leaders without formal authority
  • Experience embedding AI, automation, or workflow redesign into operating environments
  • Executive presence and EMT credibility

What the JD emphasized

  • 5+ years designing and building end-to-to architectures
  • Experience supporting AI, machine learning, or automation initiatives
  • Experience embedding AI, automation, or workflow redesign into operating environments

Other signals

  • design and build scalable data pipelines
  • prepare data for experimentation and production use
  • support metadata, lineage, and documentation practices