Data Engineer

Quick Facts

Data Engineer for AI-focused unstructured-data pipelines. Hybrid model: 1 day per week from an office in Warsaw. Budget: up to 140 PLN/h + VAT, depending on experience and service scope.

Description

Design and implement ETL and data processing services for documents, images, and audio, including extraction, OCR/ASR, and document conversion. Build embedding-driven semantic search and integrate pipelines with vector databases (in particular Qdrant). Develop event-driven processing and expose functionality through REST APIs, collaborating closely with Data Engineering and AI teams.

Responsibilities

• Build and evolve data pipelines for unstructured data in an AI project.
• Design and implement ETL flows that feed an Unstructured Data Repository and solutions used by AI agents.
• Create processing workflows for data originating from documents, images, and audio files.
• Implement content extraction mechanisms, OCR, ASR (audio transcription), and document conversion.
• Develop embedding-based capabilities: semantic search, indexing, and metadata filtering.
• Integrate pipelines with vector databases, especially Qdrant.
• Design and build data processing services using Python or Rust.
• Create and integrate REST API services to provide functional capabilities.
• Develop event-driven data processing mechanisms (e.g., Kafka or Pub/Sub).
• Collaborate on data categorization, ontology design, and AI information-processing solutions.
• Analyze and optimize performance and reliability of data processing processes.
• Work with teams delivering Data Engineering and AI solutions.

Requirements

• 3–5+ years of data engineering experience, specifically building data pipelines and data processing services.
• Strong command of Python or Rust.
• Hands-on knowledge of vector databases, especially Qdrant.
• Knowledge of embeddings, semantic search, indexing, and metadata-based filtering.
• Experience designing or developing REST API integrations.
• Knowledge of event-driven architecture (Kafka or Pub/Sub).
• Strong Linux knowledge.
• Knowledge of relational databases (e.g., PostgreSQL).
• Ability to design and develop data processing pipelines.
• Independently analyze technical issues and propose solutions.

Benefits

• Engagement in a variety of projects across Software, Embedded, Data, and Cloud Services.
• Participation in additional pre-sales initiatives and expert activities.
• Medical care, sports card, and a cafeteria platform under the applicable collaboration rules.